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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. (2016) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.4737

Regional characteristics of long-term changes in total and extreme precipitations over China and their links to atmospheric–oceanic features Fang Wanga,b and Song Yangc,d* b

a Laboratory for Climate Studies, National Climate Center, CMA, Beijing, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, China c School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China d Institute of Earth Climate and Environment System, Sun Yat-sen University, Guangzhou, China

ABSTRACT: The regional features of long-term changes in total and extreme precipitations over eight China domains and their seasonality and relationships with large-scale and local atmospheric and oceanic factors are investigated. A newly compiled data set of 917 China stations from the China National Meteorological Information Center with strict quality control is analysed. Wintertime extreme precipitation has increased persistently in all China domains, together with an obvious wetting tendency except in North China. In spring, an increase in extreme precipitation is also seen except in South China, but total precipitation increases only in half of China. A ‘northern drying and southern wetting’ (NDSW) pattern is found over eastern China for both extreme and total precipitations in summer when precipitation increases significantly in Northwest China (NWCH). Different features are found in autumn when negative trends are found in most of the China domains for total precipitation (except in NWCH and the Tibetan Plateau) and half of the domains for extreme precipitation. Overall, the frequency of extreme precipitation plays a dominant role in determining the extreme precipitation trend compared to the intensity of precipitation. The long-term changes in total precipitation and extreme ratio (extreme precipitation to total precipitation) are both strongly linked to the sea surface temperatures (SSTs) from the Indian Ocean to the Maritime Continent, the Kuroshio, the central-eastern Pacific, and part of the Atlantic Ocean. In summer, the NDSW pattern is closely associated with the weakening of East Asian monsoon circulation and the increase in land surface temperature of 40∘ –60∘ N. Local warming trends also contribute to the trends of extreme ratio in several domains in particular seasons. KEY WORDS

extreme precipitation; regional characteristics; large-scale atmospheric and oceanic features

Received 30 June 2015; Revised 7 March 2016; Accepted 9 March 2016

1. Introduction Precipitation extremes have devastating impacts on the economy and society of many countries. Observational records have shown that the number of heavy precipitation events has increased in most of land areas since about 1950 (Hartmann et al., 2013) and climate models have also projected a likely increase in the intensity and frequency of extreme precipitation in the warming future, particularly in wet seasons (Collins et al., 2013; Kirtman et al., 2013). Under the background of the upward trend of global annual precipitation (Frich et al., 2002; Alexander et al., 2006; Donat et al., 2013), large regional and seasonal diversity has been found in the trends of precipitation extremes over the world (e.g. Haylock and Nicholls, 2000; Haylock and Goodess, 2004; Yao et al., 2008; Aguilar et al., 2009; Pryor et al., 2009; Skansi et al., 2013; Villarini et al., 2013; King et al., 2014). Extreme precipitation, generally defined as the precipitation amount or the number of days exceeding certain * Correspondence to: S. Yang, School of Atmospheric Sciences, Sun Yat-sen University, 135 West Xingang Road, Guangzhou 510275, China. E-mail: [email protected]

threshold (usually certain percentiles of daily precipitations, e.g. 95% percentile), has changed remarkably over China since about 1950s, characterized by distinct regional and seasonal trend patterns (e.g. Zhai et al., 1999, 2005; Wang and Zhou, 2005; Wang et al., 2012). From a nation-wide perspective, the annual extreme precipitation over China exhibits an upward trend, although small trend has been observed for the annual total precipitation (Zhai et al., 2005; You et al., 2011; Wang et al., 2014). However, regionally, an increase in annual extreme precipitation has been reported in Northwest China (NWCH) (Zhang et al., 2012; Jiang et al., 2013), the middle and lower reaches of the Yangtze River valley (Qian and Lin, 2005; Dong et al., 2011), part of Southwest China (SWCH) (Li et al., 2015), and the coastal areas of South China (SCH) (Zhai et al., 2005), whereas decreasing trends have been observed over Northeast China (NECH) (Wang et al., 2013), North China (NCH) (Fan et al., 2012), and the Sichuan Basin (Wang and Zhou, 2005; Zhai et al., 2005). Obvious seasonal features of extreme precipitation may be further summarized as increasing trends across all seasons in NWCH, a significant increasing trend in the Yangtze River basin in winter and summer, and a decreasing trend

© 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

F. WANG AND S. YANG

in central and NCH in spring and autumn (Wang and Zhou, 2005; Zhai et al., 2005; Wang et al., 2012). Given that in many cases temporal variations and spatial patterns of extreme precipitation are similar to those of total precipitation (e.g. Wang and Zhou, 2005; Wang et al., 2014), the ratio of extreme precipitation to total precipitation (extreme ratio for short) is often used to measure the relative role of extreme precipitation in total precipitation (e.g. Wang et al., 2014). An overall increase in extreme ratio has been revealed in China (Wang et al., 2012; Wang et al., 2014; Li et al., 2015). However, how the long-term trends of total, extreme precipitation and extreme ratio vary with domains and seasons still need a comprehensive display. In addition, how the frequency and intensity of extreme precipitation events contribute to the trend of extreme precipitation, and how extreme precipitation contributes to the trend of total precipitation, still deserve a further investigation. The physical mechanisms for change in extreme precipitation are complex (O’Gorman and Schneider, 2009), and there are many influencing factors including both large-scale and local atmospheric–oceanic conditions and others. Previous studies have demonstrated the atmospheric and oceanic features in connection with the changes in extreme precipitation over China on inter-annual time scales, such as blocking high patterns, the South Asian high, the northwestern Pacific subtropical high, El Niño-Southern Oscillation (ENSO), the East Asian monsoon, and sea surface temperature (SST) anomalies (Wang and Yan, 2011; Tian and Fan, 2013; Chen and Zhai, 2014a, 2014b; Xiao et al., 2015). Connections with changes on inter-decadal or longer term scales, such as warming trend, weakening of the East Asian summer monsoon (EASM), the anti-cyclonic anomaly over northern China, and the Pacific Decadal Oscillation (PDO), have also been shown (You et al., 2011; Li et al., 2012; Qian and Zhou, 2014; Wang et al., 2014). Nevertheless, in spite of the above effort, how the large-scale and local atmospheric and oceanic conditions associated with long-term changes in total and extreme precipitations, as well as extreme ratio, vary with regions and seasons still needs further investigations. The rest of this article is organized as follows. In Section 2, data and analysis methods are introduced. The statistical features of long-term trends are given based on regional and seasonal extreme precipitation indices in Section 3, followed by their links to large-scale atmospheric and oceanic features and local factors in Sections 4 and 5, respectively. Finally, concluding remarks are provided in Section 6.

compiled data set includes 917 stations and has been processed with strict quality control, which has resolved the problems of incorrect and missing data caused by digitization and restoring of historical basic meteorological data (Ren et al., 2012). Considering that most of the stations were established after 1960s, the analysis period of this study is limited to 1961–2012. 2.1.2. Extended Reconstructed SST (ERSST) version 3b The National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST (ERSST) data set (version 3b; Smith et al., 2008) (available at https://www.ncdc.noaa.gov/data-access/marineocean-data /extended-reconstructed-sea-surface-temperature-ersstv3b) is also used in this study. This monthly analysis is available from January 1854 to the present, and includes anomalies computed with respect to the 1971–2000 monthly climatology, with a resolution of 2∘ × 2∘ latitude and longitude. The ERSST v3b is optimally tuned to exclude under-sampled regions for global averages, and it does not include satellite data. 2.1.3. GHCN + CAMS land surface air temperature analysis The global land surface air temperature (Ts) was obtained from the NOAA Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN + CAMS) data set version 3.01 (Fan and van den Dool, 2008), which is available at http://www. esrl.noaa.gov/psd/data/gridded/data.ghcncams.html. This monthly data set combines two sets of station observations from GHCN and CAMS, and uses some unique interpolation methods to reasonably capture the most common temporal–spatial features in the observed climatology and anomaly fields. This data set is at a resolution of 0.5∘ × 0.5∘ latitude/longitude from 1948 to the present. 2.1.4. NCEP/NCAR Reanalysis The National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) Reanalysis (Kalnay et al., 1996) is employed to obtain monthly winds, vertical velocity, and water vapour fields. These data were from 1948 to the present at a 2.5∘ × 2.5∘ latitude/longitude resolution, and can be downloaded from http://www.esrl.noaa.gov/ psd/data/gridded/data.ncep.reanalysis.derived.html. 2.2. Analysis methods 2.2.1. Data preprocessing and spatial domains

2.

Data and methodology

2.1. 2.1.1.

Data Daily precipitation over China

Daily precipitation over China is obtained from the National Meteorological Information Center (NMIC), the China Meteorological Administration (CMA). This newly

Among the 917 stations, only those with three or less days of missing data, i.e. 676 stations, are chosen. Given that data inhomogeneity may result in incredible precipitation trends, the RHtestsV4 software package, based on the transPMFred algorithm (Wang et al., 2010), is used to test the homogeneity of daily precipitation in the 676 stations. Significant change points are found in 66 stations. After

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Int. J. Climatol. (2016)

REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

removal of those inhomogeneous stations, the remaining 610 stations are used for the further analysis (Figure 1). Clearly, stations are much denser in eastern China than in western China. Compared to Wang et al. (2014), who analysed the extreme precipitation averaged over the entire China, 81 additional stations and three more years of data are used in the present study. As in Luo et al. (2002), eight domains or ‘climate divisions’ are recognized in China (Figure 1): NWCH, Big Bend of Yellow River (BBYR), NECH, NCH, Yangtze–Huaihe River Basin (YHRB), SCH, SWCH, and Tibetan Plateau (TP). It should be noted that the present division for domains, which has been applied in a number of research (Li et al., 2005), and may not be superior to other division methods such as clustering method and the rotated empirical orthogonal function analysis that have also used to obtain precipitation division (Qian and Qin, 2008). Different division methods may result in uncertainties of different degrees in precipitation analysis. 2.2.2. Extreme precipitation indices and trend test In this study, we mainly focus on three precipitation indices: total precipitation (Ptot ), extreme precipitation (P95p ), and the ratio of extreme to total precipitation (R95p ), to quantify extreme precipitation and its relative importance in total precipitation. Qualitatively, a relative increase in extreme (total) precipitation tends to result in a positive (negative) trend of extreme ratio, and vice versa. In addition, we also include indices such as daily precipitation frequency and intensity, for both extreme and total precipitation, to better understand the changes in the three main indices. The definitions of all these indices are listed in Table 1, which are calculated as the means for various seasons, i.e. winter (December– January–February, DJF), spring (March–April–May, MAM), summer (June–July–August, JJA), and autumn (September–October–November, SON). The Mann–Kendall (MK) nonparametric method (Mann, 1945; Kendall, 1955) is used to test whether a statistically significant trend exists in a particular time series. To eliminate the autocorrelation of original series on MK test and trend estimation, an iteration scheme including de-trended and pre-whitening method is used to obtain the optimal estimator of trends (Wang and Swail, 2001). 2.2.3. Data processing The extreme precipitation indices are first calculated at each station and then interpolated to regular grids of 0.5∘ × 0.5∘ latitude and longitude using the angular distance weighting (ADW) algorithm, one of the most appropriate methods to interpolate irregularly spaced data to regular grids (New et al., 2000). Finally, the regional mean series of these indices are constructed for the various domains using the area-weighted average based on the interpolated grids. Considering the large spatial variation of precipitation indices, normalized indices are used for a convenient comparison among the domains.

To investigate the contribution of trend of a precipitation index from its components [say, a precipitation index (P) can be expressed as the sum (A + B) or product (A*B) of other two indices (say A and B)], the following relations can be easily obtained based on the linear theory: L (A + B) = L (A) + L (B)

(1)

L (A ∗ B) = Bm ∗ L (A) + Am ∗ L (B) [( ) ( )] + L A − Am ∗ B − Bm

(2)

where L denotes the linear trend operator. Am and Bm are multi-year mean of A and B, respectively. Obviously, the first and second terms on the right-hand side of Equation (2) indicate the contribution from long-term trends of A and B, respectively. The third term indicates the contribution from co-variation of A and B. The relative contributions (%) of the right-hand side terms are then calculated divided by their absolute sum. Correlation analysis is used to examine the possible links of changes in regional precipitations to large-scale atmospheric and oceanic features and local dynamic and thermodynamic factors. We carry out the analysis for both original (including both long-term and inter-annual variations) and de-trended (including only inter-annual variations) precipitation indices, and compare the correlation patterns to distinguish the large-scale features associated with long-term precipitation changes from those with inter-annual variations. 3.

Long-term trends of regional precipitations

Figure 2 shows the trends of normalized extreme precipitation indices, from which regional and seasonal variations over China can be found. As a whole, extreme and total precipitations over China generally exhibit positive trends in all seasons except the negative trends in fall. In winter, obvious wetting tendencies are seen in most of China except for NCH. Meanwhile, extreme precipitation has increased in all domains (Figure 2(a)). These trends are found to be particularly significant in northern China (NWCH, BBYR, and NECH) and YHRB (Table 2). In spring, extreme precipitation increases in all domains but SCH, being significant in NWCH, TP, and NCH, although total precipitation increases in only half of the domains (Figure 2(b), Table 2). In summer, a typical ‘northern drying and southern wetting’ (NDSW) pattern exhibits over eastern China for both extreme and total precipitations (Figure 2(c)), associated with the inter-decadal variation of precipitation (Hu, 1997; Ding et al., 2008). In addition, a significant increase is also detected in NWCH. In autumn, negative trends are seen in most domains except NWCH and TP for total precipitation, and half of the domains for extreme precipitation (Figure 2(d)). Significant trend only appears in SWCH, for both total and extreme precipitations (Table 2). Overall, the precipitation trends presented in Figure 2 are consistent with those shown in Hu et al. (2003).

© 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Int. J. Climatol. (2016)

F. WANG AND S. YANG 60°N

50°N

40°N

30°N

20°N

70°E

80°E

90°E

100°E

110°E

120°E

130°E

140°E

Figure 1. Locations of 676 China stations and domains of analysis.

Table 1. Definition of precipitation indices. Index

Name

Definition

Unit

Ptot P95p R95p F tot F 95p I tot I 95p

Total precipitation Extreme precipitation Ration of extreme to total precipitation Frequency of rainy days Frequency of extreme precipitation days Intensity of rainy days Intensity of extreme precipitation days

Seasonal total precipitation in wet days (R ≥ 0.1 mm) Seasonal total precipitation in extreme precipitation days (R > 95p) Ratio of P95p to Ptot Seasonal count of wet days Seasonal count of extreme precipitation days Ptot /F tot P95p /I 95p

mm mm % day day mm day−1 mm day−1

R is daily precipitation. 95p indicates the 95th percentile of precipitation on wet days of any season in 1971–2000.

In general, the long-term trends of extreme and total precipitations are synchronous (Figure 2, Table 2), except in winter for NCH, spring for BBYR, SWCH, and YHRB, summer for BBYR and SWCH, and autumn for YHRB and SCH. Although un-synchronous features happen to more domains in spring than other seasons, it is particularly clear that total precipitation decreases but extreme precipitation increases in SWCH in JJA. Such result is interesting due to increased extreme precipitation under the background of frequent severe droughts (Chen and Zhai, 2014a, 2014b), implying an accelerated hydrological cycle featured by increases in intense precipitation and long-lasting droughts. It is further noted that the extreme ratio has increased for more cases (over 80%), particularly in winter and spring when positive trends are detected in all domains. The following three combinations may contribute to a positive trend of extreme ratio. First, both extreme and total precipitations show positive trends, but extreme precipitation increases relatively faster. Second, both extreme and total precipitations show negative trends, but extreme precipitation decreases relatively slower. Third, extreme precipitation trend is positive but total precipitation changes oppositely. These three cases may reflect a complex change in precipitation intensity spectra, which may vary from global to regional scales (Liu et al., 2009; Shiu et al., 2012; Wu and Fu, 2013). The trends of the frequency and intensity of extreme and total precipitations (Figure 3) are overall consistent with those of the amount of extreme and total

precipitations (Figure 2), especially for extreme precipitation. This feature indicates a connection of the long-term changes in frequency and amount of precipitation. Furthermore, the trends of extreme precipitation frequency generally dominate over the trends of extreme precipitation amount (Figure 4). Another question needs to answer is whether extreme precipitation plays a dominant role in determining the long-term trends of total precipitation. Comparing the relative contributions of extreme and non-extreme precipitations to total precipitation trend (Figure 5) indicates that the dominant role of extreme precipitation appears mainly for a few cases with positive total precipitation trends in the southern domains (i.e. YHRB and SCH) in DJF and JJA, SWCH in DJF, and NCH in MAM. In contrast, negative total precipitation trends often result from a predominant decrease in non-extreme precipitation. These features imply an increasing risk of summer floods from extreme precipitation in southern China, and drought from decrease in non-extreme precipitation associated possibly with the overall decreasing trend in light rain events (Qian et al., 2007).

4. Links to large-scale atmospheric and oceanic backgrounds How the long-term changes in total and extreme precipitations link to large-scale circulation backgrounds is

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Int. J. Climatol. (2016)

REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Linear trends for subregions in China 0.040

0.040

0.020

0.020

0.000

0.000

–0.020 –0.040

–0.020

(a) DJF

–0.040

0.040

0.040

0.020

0.020

0.000

0.000

–0.020 –0.040

(b) MAM

–0.020

(c) JJA

–0.040

(d) SON

Figure 2. Normalized linear trends of seasonal precipitation indices (Ptot , P95p , and R95p ) in eight domains and entire China (CN): (a) DJF, (b) MAM, (c) JJA, and (d) SON. * indicates the trends passing the significance test at the 95% confidence level (Student’s t-test)

Table 2. Linear trends of seasonal indices (1961–2012). Indices DJF Ptot P95p R95p F tot F 95p I tot I 95p MAM Ptot P95p R95p F tot F 95p I tot I 95p JJA Ptot P95p R95p F tot F 95p I tot I 95p SON Ptot P95p R95p F tot F 95p I tot I 95p

NWCH

BBYR

NECH

TP

SWCH

NCH

YHRB

SCH

CN

0.76a 0.26a 2.57a 0.25a 0.03a 0.11a 0.13

0.56a 0.11a 1.21 0.17 0.02a 0.07 −0.06

1.11a 0.37a 3.05a 0.36a 0.05a 0.08a 0.11

0.42 0.13 0.70 0.20 0.02 −0.04 −0.86

0.72 0.32 0.03 0.04 0.01 0.06 0.27

−0.21 0.03 0.80 0.00 0.00 0.02 0.05

6.50 3.42a 1.65a 0.34 0.10a 0.18a 0.55

2.09 1.80 1.49 −0.28 0.04 0.25 0.91

1.61 0.77a 1.27a 0.21 0.04a 0.06 −0.09

1.27 0.58a 1.48a 0.18 0.03a 0.08 0.12

−0.52 0.31 0.84 −0.05 0.02 0.00 −0.14

2.14 0.65 0.22 0.33 0.03 0.05 0.04

7.10a 3.10a 1.66a 0.77a 0.16a 0.18a −0.06

−1.44 0.43 0.13 −0.21 0.02 −0.04 −0.21

2.98 1.95a 1.92a 0.01 0.06 0.20 0.44

−6.88 0.47 0.41 −0.77a −0.01 0.10 0.53

−5.59 −0.84 0.15 −0.48 −0.01 0.03 0.11

0.59 0.73 0.50a 0.06 0.03a 0.01 −0.71a

3.09a 1.01a 0.80 0.38a 0.04a 0.09a 0.15

−0.28 0.39 0.29 −0.30 0.00 0.13a −0.08

−5.19 −1.19 0.13 −0.67 −0.02 0.07 0.02

−1.20 −0.28 −0.09 −0.10 −0.03 −0.01 0.07

−4.50 2.66 0.55a −0.54a 0.03 0.10 0.25

−10.72a −3.78 −0.05 −0.75a −0.06 0.00 0.21

16.73a 9.84a 1.05a 0.33 0.09a 0.40a 0.64

4.06 4.09 0.36 −0.23 0.04 0.20 0.39

0.04 1.66 0.42a −0.23a 0.02 0.12a 0.48

0.92 0.23 0.06 0.19 0.02 0.06 −0.07

−3.43 −0.51 0.13 −0.47 −0.01 −0.04 −0.42

−1.42 −0.54 0.11 −0.14 −0.02 0.00 −0.13

2.42 0.18 −0.24 0.15 0.02 0.05 0.17

−11.77a −4.32a −0.09 −1.03a −0.09a −0.06 0.09

−1.72 −0.21 0.16 −0.26 0.01 0.12 −0.20

−4.62 0.01 0.20 −0.61 −0.01 0.12 −0.22

−9.23 0.76 1.01 −0.92a 0.00 0.50a 1.77

−2.67 −0.47 0.26 −0.31a −0.01 0.02 −0.31

Units: mm decade−1 for Ptot , P95p , I tot , and I 95p ; % decade−1 for R95p ; day decade−1 for F tot and F 95p . a Trends passing significance test at the 95% confidence level. © 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Int. J. Climatol. (2016)

F. WANG AND S. YANG

Linear trends for subregions in China 0.040

0.040

0.020

0.020

0.000

0.000

–0.020

–0.020

–0.040

(a) DJF

–0.040

0.040

0.040

0.020

0.020

0.000

0.000

–0.020 –0.040

(b) MAM

–0.020

(c) JJA

–0.040

(d) SON

Figure 3. Normalized linear trends of seasonal precipitation frequency (F tot and F 95p ) and intensity (I tot and I 95p ) in eight domains and entire China (CN): (a) DJF, (b) MAM, (c) JJA, and (d) SON. * indicates the trends passing the significance test at the 95% confidence level (Student’s t-test).

Relative Contributions (%) 120

120

80

80

40

40

0

0

–40

–40

–80

(a) DJF

–80

–120

–120

120

120

80

80

40

40

0

0

–40

–40

–80

(c) JJA

–120

–80

(b) MAM

(d) SON

–120

Figure 4. Relative contributions to trend of extreme precipitation (P95p ) from extreme precipitation intensity (I 95p ), frequency (F 95p ), and their co-variation: (a) DJF, (b) MAM, (c) JJA, and (d) SON. Unit: %.

investigated here by examining the relationship between regional precipitation and large-scale circulation fields [e.g. SST, Ts, and 850-hPa winds (U850)]. Calculations are computed for the changes in both original and de-trended precipitation, respectively, to distinguish the signals associated with long-term changes from those with inter-annual changes (Wang et al., 2014). To better understand the correlation patterns, we examine the long-term trends of seasonal SST, land Ts, and U850 (Figure 6). First, an overall increase in Ts appears

over most of land areas, especially in winter and spring. Second, in spite of a relatively smaller rate, SST also increases markedly over most oceanic areas, characterized by a strong SST increase in subtropical oceans [particularly over the path of western boundary currents (Wu et al., 2012)], a basin-wide warming in the Indian and Atlantic oceans, and a warming in the western and eastern Pacific. The warming in the eastern Pacific and the Atlantic may be linked to the phase shift of PDO and the Atlantic Multi-decadal Oscillation (AMO) in the

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Int. J. Climatol. (2016)

REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Relative contributions to total trend (%) (a) DJF

80 40

40

0

0

–40

–40

–80

–80

(c) JJA

80

(b) MAM

80

(d) SON

80

40

40

0

0

–40

–40

–80

–80

Figure 5. Relative contributions to the trend of total precipitation (Ptot ) from extreme precipitation (P95p ) and non-extreme precipitation (Ptot − P95p ): (a) DJF, (b) MAM, (c) JJA, and (d) SON. Unit: %.

Linear trends of TS (°c decade–1) and U850 (m s–1 decade–1) 1961–2012 (a) DJF

(b) MAM

1

80°N 60°N 40°N 20°N 0 20°S 40°S 60°S

1

80°N 60°N 40°N 20°N 0 20°S 40°S 60°S 0

60°E

120°E

180

120°W

60°W

0

0

60°E

120°E

180

120°W

60°W

1

0 1

80°N 60°N 40°N 20°N 0 20°S 40°S 60°S

80°N 60°N 40°N 20°N 0 20°S 40°S 60°S 0

–0.5

60°E

–0.4

120°E

–0.3

–0.2

180

120°W

–0.16

–0.12

60°W

–0.08

–0.04

0

0

0

0.04

60°E

0.08

120°E

0.12

0.16

180

0.2

120°W

0.3

60°W

0.4

0

0.5

Figure 6. Linear trends (1961–2012) of seasonal-mean SST/Ts (shadings; ∘ C decade−1 ) and U850 (vectors; ms−1 decade−1 ): (a) DJF, (b) MAM, (c) JJA, and (d) SON. Green polygons refer to the domains of China.

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Int. J. Climatol. (2016)

F. WANG AND S. YANG

Total Precp DJF NWCH

1

NWCH

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(a)

1

(b)

60°S 0

60°E 120°E

180 120°W 60°W

BBYR

0

0

1 60°N

30°N

30°N

0

0

30°S

30°S (c) 0

60°E 120°E

180 120°W 60°W

NECH

0

30°N

0

0

30°S

30°S

180 120°W 60°W

60°E 120°E

180 120°W 60°W

NECH 60°N

60°E 120°E

(d) 0

30°N

0

0.358

1

(c)

0

60°E 120°E

180 120°W 60°W

60°N

30°N

30°N

0

0

30°S

30°S

(g) 60°E 120°E

180 120°W 60°W

–0.233

(h)

60°S

0

0

60°E 120°E

180 120°W 60°W

1 60°N

30°N

30°N

0

0

30°S

30°S

(i) 0

60°E 120°E

180 120°W 60°W

0

–0.276

1

60°N

60°S

0 1

60°N

0

0.276

0.233

60°S 0

0 1

(f)

1

60°S

0 1

60°S

60°N

60°S

180 120°W 60°W

BBYR

60°N

60°S

60°E 120°E

(j)

60°S

0

0

60°E 120°E

180 120°W 60°W

1

–0.358

0 1

60°N

60°N

30°N

30°N 0

0 30°S

(k)

60°S 0

60°E 120°E

180 120°W 60°W

0

30°S

(l)

60°S 0

60°E 120°E

180 120°W 60°W

0

Figure 7. Correlations of DJF total precipitation (Ptot ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR, NECH, TP, YHRB, and SCH, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.233, 0.276, and 0.358, respectively.

recent decades (d’Orgeville and Peltier, 2007). Third, changes in U850 feature a remarkable weakening of trade winds over the central to eastern tropical Pacific in all seasons (Figure 6(a)–(d)) and a significant weakening of the EASM (Figure 6(c)). In addition, an anomalous anti-cyclonic trend is noted in northern China. All these features may be linked to the long-term changes of precipitation over China, which will be addressed in the following sections. 4.1.

Winter

Figure 7 shows the correlation patterns of SST, Ts, and U850 linked to winter total precipitation over China

(only the domains with significant correlation patterns are shown, similarly hereinafter). Although obvious differences appear from the figure, two common features can be identified. First, total precipitation in northern China (i.e. NWCH and NECH), with most remarkable relative trends (Figure 2(a)), is significantly and positively correlated with SSTs from the Indian Ocean to the Maritime Continent, Kuroshio and its extension region, and part of the Atlantic Ocean (Figure 7(a) and (e)). It is noted that these correlation patterns generally disappear after the trend of total precipitation is removed, indicating a close link of the long-term changes in total precipitation to the SST warming in the above oceanic areas. Secondly, typical

© 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Int. J. Climatol. (2016)

REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Extreme ratio DJF

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

60°S 0

60°E 120°E

180 120°W 60°W

0

60°S

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(c)

60°S 0

60°E 120°E

180 120°W 60°W

60°N 30°N

0

0

30°S

30°S

(e) 60°E 120°E

180 120°W 60°W

0

60°S

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(g)

60°S 0

60°E 120°E

180 120°W 60°W

60°E 120°E

180 120°W 60°W

0

0.358

(d) 0

30°N

0

0

60°S 0

60°N

60°S

(b)

60°E 120°E

180 120°W 60°W

0

0.276

0.233 (f) 0

60°E 120°E

180 120°W 60°W

0

–0.233

(h)

60°S 0

0

60°E 120°E

180 120°W 60°W

0

–0.276 60°N

60°N

30°N

30°N

0

0

30°S

30°S

(i) 0

60°E 120°E

180 120°W 60°W

0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

–0.358

(j)

60°S

60°S

(k) 0

60°E 120°E

180 120°W 60°W

0

60°S

0

60°E 120°E

180 120°W 60°W

0

60°E 120°E

180 120°W 60°W

0

(l) 0

Figure 8. Correlations of DJF extreme precipitation ratio (R95p ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR, NECH, TP, YHRB, and SCH, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.233, 0.276, and 0.358, respectively.

El Nino-like SST patterns are associated with the winter total precipitation in TP, YHRB, and SCH (Figure 7(g), (i), and (k)). However, these relationships mainly exist on inter-annual time scales by comparing the result with de-trended patterns (Figure 7(h), (j), and (l)). In addition, the long-term positive trend of winter total precipitation in YHRB is significantly linked to the warming of the Indian Ocean (Figure 7(i) and (j)). The correlation patterns for extreme precipitation (figure is not shown) are similar to those for total precipitation as shown in Figure 7, except in BBYR where extreme precipitation trend is closely linked to the increase in local

Ts. It is found from Figure 8 that only limited correlation patterns (of SSTs in part of the Indian and Atlantic oceans, as well as the Kuroshio and its extension region) are associated with the long-term changes in the extreme ratio in NWCH, NECH, and YHRB. It is also found that there exists a remarkable link between the long-term trends of extreme ratio and local Ts in BBYR. 4.2.

Spring

In spring, a significant increase in total and extreme precipitations occurs in northern China (NWCH, NECH, and NCH) and TP. The warming trends over the Indian Ocean

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F. WANG AND S. YANG

Total precp MAM

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

0

60°E 120°E

180 120°W 60°W

60°S 0 0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S 0

60°E 120°E

180 120°W 60°W

60°N

30°N

30°N

0

0

30°S

30°S

180 120°W 60°W

0

0.354

60°S 0 0

60°N

60°E 120°E

60°E 120°E

180 120°W 60°W

0

0.273

0.231

60°S

60°S 0

60°E 120°E

180 120°W 60°W

0

0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°E 120°E

180 120°W 60°W

0

–0.231

60°S

60°S 0

60°E 120°E

180 120°W 60°W

0

0

60°E 120°E

180 120°W 60°W

0

–0.273 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S 0

60°E 120°E

180 120°W 60°W

60°S 0 0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S 0

60°E 120°E

180 120°W 60°W

60°S 0 0

–0.354 60°E 120°E

180 120°W 60°W

0

60°E 120°E

180 120°W 60°W

0

Figure 9. Correlations of MAM total precipitation (Ptot ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR, NECH, TP, NCH, and YHRB, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.231, 0.273, and 0.354, respectively.

and the Kuroshio and its extension favour the positive trends of total precipitation in NWCH, NECH, and NCH. In TP, a linkage between total precipitation and an El Nino-like SST pattern appears on inter-annual time scale (Figure 9(h)), while its long-term change is significantly connected to the increase in SST/Ts around 30∘ N, surface warming over the Eurasian continent, and warming over the western Pacific warm pool and the oceans to the east of Australia. Similar patterns are also found for extreme precipitation, except that stronger connections are found between Indian

Ocean warming and the increasing trends of extreme precipitation in NWCH and NCH, as well as between the SST over the Kuroshio and its extension and the extreme precipitation in NWCH and BBYR (not shown). These features may be partly reflected in the patterns associated with extreme ratio, wherein Indian Ocean warming is significantly linked to the long-term trends of extreme ratio in NWCH, BBYR, NECH, NCH, and YHRB (Figure 10). This result implies that the signals associated with extreme precipitation suppress those associated with total precipitation.

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REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Extreme ratio MAM

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

60°S 0

60°E 120°E 180 120°W 60°W

(b)

60°S

0

0

60°E 120°E

180 120°W 60°W

0

0.354 60°N

60°N 60°N

30°N

30°N 30°N

0 30°S

(c)

60°S 0

60°E 120°E 180 120°W 60°W

0

0 0 30°S 30°S 60°S 0 60°E 120°E

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(e)

60°S 0

60°E 120°E 180 120°W 60°W

60°N 30°N

0

0

30°S

30°S

(g) 0

60°E 120°E 180 120°W 60°W

60°E 120°E

180 120°W 60°W

0

–0.231

(h)

60°S

0

0.273

0.231 0

30°N

0

(f)

60°S

0

60°N

60°S

(d) 180 120°W 60°W

0

60°E 120°E

180 120°W 60°W

0

–0.273 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(i) 0

60°E 120°E 180 120°W 60°W

0

60°S

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(k) 0

60°E 120°E 180 120°W 60°W

0

60°S

(j) 0

60°E 120°E

180 120°W 60°W

0

60°E 120°E

180 120°W 60°W

–0.354

0

(l) 0

Figure 10. Correlations of MAM total precipitation (R95p ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR, NECH, TP, NCH, and YHRB, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.23, 0.27, and 0.35, respectively.

4.3. Summer Figures 11 and 12 show the patterns of SST/Ts and U850 that are significantly associated with total precipitation and extreme ratio, respectively. As expected, significant and positive (negative) pattern of U850 over East Asia is related to the total precipitation in NCH (YHRB), indicating more (less) rainfall with strong (weak) EASM in NCH (YHRB) (Figure 11(e) and (g)). The patterns become weaker after the trend of total precipitation is removed (Figure 11(f) and (h)), implying that the NDSW pattern is closely linked to the weakening of EASM as shown in Figure 6(c). The pattern

is also associated with the warming of central-eastern tropical Pacific and the accompanied weakening of trade winds (Figure 11(e)–(h)), which may be linked to the shift of PDO from a cool phase to a warm phase in the past decades (Yu et al., 2015). In addition, the increasing trend of total precipitation in YHRB is significantly related to the increase in Ts between 40∘ and 60∘ N, which tends to strengthen the zonal land–sea thermal contrast and contribute to the weakening of EASM circulation (Zhu et al., 2012) and to the warming in the tropical Indian Ocean, the western Pacific, and part of the Atlantic Ocean. This effect is combined with

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F. WANG AND S. YANG

Total Precp JJA

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

60°S 0

60°E 120°E

180

120°W 60°W

0

0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(c)

60°S 0

60°E 120°E

180

120°W 60°W

(b)

60°S 60°E 120°E

180

120°W 60°W

0

0.354

0.273

(d)

0.231

60°S

0

0

60°E 120°E

180

120°W 60°W

0

–0.231 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(e) 0

60°E 120°E

180

120°W 60°W

0

60°S

–0.273 (f) 0

60°E 120°E

180

120°W 60°W

0

–0.354 60°N

60°N

30°N

30°N

0

0

30°S

30°S

(g)

60°S 0

60°E 120°E

180

120°W 60°W

0

(h)

60°S 0

60°E 120°E

180

120°W 60°W

0

Figure 11. Correlations of JJA total precipitation (Ptot ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, SWCH, NCH, and YHRB, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.231, 0.273, and 0.354, respectively.

the concurred tropical central-eastern Pacific warming, which is also linked to the weakening of EASM (Yang and Lau, 2004; Zhou et al., 2009; Li et al., 2010). However, model simulations have shown that the warming over central-eastern Pacific plays a predominant role in EASM weakening (Li et al., 2010; Fu and Li, 2013), whereas the Indian Ocean warming only plays an unimportant or even opposite role (Li et al., 2008; Fu and Li, 2013). The significant long-term increase in NWCH total precipitation is linked to the warming from the Indian Ocean to the South China Sea and the weakening of trade winds over the eastern Pacific (Figure 11(a) and (b)). Similar SST/Ts and U850 patterns are also found for extreme precipitation (not shown). The extreme ratio, however, shows different features (Figure 12). Although significant patterns are found for both total and extreme precipitations in NWCH (Figure 11(a)), extreme ratio does not inherit these patterns, indicating that the signals of total and extreme precipitations offset each other. In contrast, the extreme ratio in YHRB shares most features of the correlation patterns of total and extreme precipitations (Figure 11(g)), indicating the dominant role of extreme precipitation in determining the long-term change in extreme ratio. In addition, the warming from the Indian Ocean to the western Pacific, and in the eastern Pacific,

as well as the corresponding EASM weakening, are found to be associated with the long-term change of extreme precipitation (data not shown) but total precipitation in SWCH (Figure 11(c) and (d)). As a result, significant features are associated with extreme ratio, as with extreme precipitation (Figure 12(c) and (d)), resulted from the decrease in total precipitation and the increase in extreme precipitation (Figure 2(c)). 4.4. Autumn The large-scale features associated with the long-term changes in autumn precipitation are relatively insignificant compared to other seasons due to less significant long-term changes in precipitation (Figure 2(d)). However, the patterns related to the long-term trend of NWCH total precipitation again are pointed to warming in the Indian Ocean warming, as well as the western Pacific, the Kuroshio and its extension, and the Atlantic Ocean (Figure 13(a) and (b)). The relationship becomes weaker for extreme precipitation (data not shown) and is no longer important for extreme ratio (Figure 14(a) and (b)). The negative trends for both total and extreme precipitations in SWCH (Figure 2(d)) are closely linked to the weakening trends of northeastward U850 with a significant negative correlation (Figure 6(d)), and also to the warming of the

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REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Extreme ratio JJA

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

60°S

(b)

60°S 0

60°E 120°E

180

120°W 60°W

0

0

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(c)

60°S 0

60°E 120°E

180

120°W 60°W

0

60°S

60°E 120°E

180

120°W 60°W

0

0.354

0.273

(d) 0

60°E 120°E

180

120°W 60°W

0.231

0

–0.231 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(e) 0

60°E 120°E

180

120°W 60°W

0

60°S

–0.273 (f) 0

60°E 120°E

180

120°W 60°W

0

–0.354 60°N

60°N

30°N

30°N

0

0

30°S

30°S

(g)

60°S

(h)

60°S 0

60°E 120°E

180

120°W 60°W

0

0

60°E 120°E

180

120°W 60°W

0

Figure 12. Correlations of JJA total precipitation (R95p ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, SWCH, NCH, and YHRB, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.231, 0.273, and 0.354, respectively.

Indo-Pacific oceans, the waters to the east of Australia, and the Atlantic Ocean (Figure 15(e) and (f); figures for extreme precipitation not shown). The contributions of total and extreme precipitations to extreme ratio are greatly offset, leading to nearly no trend for the extreme ratio in SWCH, so as for the long-term patterns of Ts/SST and U850 winds (Figure 14(e) and (f)). In addition, the negative trend of total precipitation in BBYR is mainly associated with the Indian Ocean warming (Figure 13(c) and (d)), whereas extreme ratio trend in BBYR is linked to the warming in the western Pacific, the oceans to the east of Australia, and part of the Atlantic Ocean (Figure 14(c) and (d)). The total and extreme precipitations in BBYR are also linked to an ENSO-like pattern, but mainly on inter-annual time scales.

5. Local and other factors associated with the long-term changes in regional precipitations 5.1. Local factors Figure 15 shows the long-term trends of Ts, 500-hPa vertical velocity (𝜔), and precipitable water (PRW). In the past decades, Ts increases in all seasons and domains.

PRW and 𝜔 generally change according to a relationship of positive (negative) PRW trend with increased (decreased) 𝜔, both favouring (suppressing) precipitation. Meanwhile, exceptions also occur over BBYR and TP in winter, NWCH, TP, and SWCH in spring, and TP, YHRB, and SCH in autumn, tending to provide opposite contributions to precipitation. The relationships between precipitation (measured by both original and de-trended indices) and local factors are shown in Figure 16. Empirically, the relationship between Ts and extreme precipitation follows the Clausius–Clapeyron (CC) rate (∼7% per ∘ C). However, this rate greatly depends on the time scale of precipitation variations (e.g. daily or hourly) and is a function of temperature (or latitude) (Utsumi et al., 2011), modulated by atmospheric dynamics (e.g. O’Gorman and Schneider, 2009). In this study, which focuses on seasonal scale, indicates a negative correlation between precipitation and Ts for JJA but a positive correlation for other seasons. Meanwhile, PRW (𝜔) is always positively (negatively) correlated with precipitations, implying that an increase in water vapour content or intensity of 𝜔 favours precipitation. Of more interest is whether and how the long-term changes in these local factors are related to those in

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Int. J. Climatol. (2016)

F. WANG AND S. YANG

Total Precp SON

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

0.354

(b)

60°S

60°S 0

60°E 120°E

180

120°W 60°W

0

0

60°E 120°E

180

120°W 60°W

0

0.273 60°N

60°N

30°N

30°N

0

0

30°S

30°S

(c)

60°S 0

60°E 120°E

180

120°W 60°W

0

60°S

0.231 –0.231 (d) 0

60°E 120°E

180

120°W 60°W

0

–0.273 –0.354

60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(e) 0

60°E 120°E

180

120°W 60°W

0

60°S

(f) 0

60°E 120°E

180

120°W 60°W

0

Figure 13. Correlations of SON total precipitation (Ptot ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR and SWCH, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.231, 0.273, and 0.354, respectively.

precipitation indices. Comparing the correlation between original and de-trended precipitation indices (Figure 16) indicates that the long-term warming trends are closely linked to the long-term increase in extreme ratio as seen in NWCH and BBYR for winter, TP and SWCH in spring, and BBYR in autumn. This feature can be interpreted by the un-proportional changes in precipitation of different intensity bins with temperature. However, it may also vary with spatial scales (Liu et al., 2009; Shiu et al., 2012; Wu and Fu, 2013). Another noteworthy feature is that the local Ts increase in summer is linked to the NDSW pattern of total precipitation. That is, a weaker (stronger) negative correlation between total precipitation and Ts is detected in NCH and NECH (YHRB) after the trends of total precipitation are removed (Figure 16). It is also interesting that the total precipitation in SWCH is negatively correlated to the Ts in all seasons, but only in autumn a link between their long-term changes is detected. As mentioned above, PRW and 𝜔 tend to have similar contributions to precipitation in most cases (i.e. PRW and 𝜔 increase or decrease concurrently) (Figure 15). Two more notable characteristics need be addressed. First, the significant 𝜔 increasing trend in NWCH (Figure 15) has dominant contributions to the long-term increase in total precipitation in all seasons, as well to the positive trends of extreme precipitation and its ratio in DJF and MAM. However, remarkable anti-cyclonic trends of U850 are observed in NWCH and its adjacent northern areas (Figure 6), which is unfavorable for an increase of 𝜔 intensity. This feature may be interpreted by the enhanced southern wind to the western flank of the anomalous anti-cylonic

winds and water vapour transport from the Arabian Sea to the north of NWCH (Shi et al., 2007), as well as the distinctive topography in NWCH, which may exert a lifting effect on surface winds. Secondly, although a summer NDSW pattern is found for both total and extreme precipitations, the contributions from PRW and 𝜔 may be different. In NCH, the total precipitation decreasing trend is contributed by the reduction of both PRW and 𝜔 intensity, while the long-term extreme precipitation decrease is mainly associated with the weakening trend of 𝜔. In YHRB, the contribution of PRW to precipitation is mainly on inter-annual time scales other than long-term changes. Meanwhile, the significant long-term increase in 𝜔 intensity mainly contributes to the positive trend of extreme precipitation other than total precipitation (Figures 15 and 16). The linkage of increasing trend of extreme precipitation with 𝜔 may imply a potential increase in convective precipitation (generally dominates the extreme precipitation events) in YHRB, which may be much more sensitive to Ts increase than stratiform precipitation (Berg et al., 2013). 5.2. Discussion In this study, we have only analysed the links between limited circulation features of SST/Ts and U850 with the long-term changes in precipitation. Other circulation factors such as the Arctic Oscillation (AO), PDO, and AMO also contribute to the long-term precipitation trend over China (Li et al., 2005; Ma, 2007; Zhou et al., 2009; Mao et al., 2011; Qian and Zhou, 2014; Qian et al., 2014). However, the changes in circulation factors are not independent, which may often be mutually adjusted and connected

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REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS

Extreme ratio SON

60°N

60°N

30°N

30°N

0

0

30°S

30°S

(a)

(b)

0.354

60°S

60°S 0

60°E 120°E

180

120°W 60°W

0

0

60°E 120°E

180

120°W 60°W

0

0.273 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(c) 0

60°E 120°E

180

120°W 60°W

60°S

0

0.231 –0.231 (d) 0

60°E 120°E

180

120°W 60°W

–0.273

0

–0.354 60°N

60°N

30°N

30°N

0

0

30°S

30°S

60°S

(e) 0

60°E 120°E

180

120°W 60°W

0

60°S

(f) 0

60°E 120°E

180

120°W 60°W

0

Figure 14. Correlations of SON total precipitation (R95p ) over domains of China with SST and Ts (shadings) and with U850 (vectors). Left panels are for original precipitations and right panels are for de-trended precipitations. Panels from top to bottom refer to NWCH, BBYR and SWCH, respectively. The values that are statistically significant at the 90, 95, and 99% confidence levels are 0.231, 0.273, and 0.354, respectively.

Linear trends for subregions in China

(a)

(b)

(c)

(d)

Figure 15. Normalized linear trends of seasonal Ts, precipitable water, and 500-hPa vertical velocity in eight domains and entire China (CN): (a) DJF, (b) MAM, (c) JJA, and (d) SON.

corresponding to the global warming phenomenon. For example, a relationship has been demonstrated between PDO and AMO (d’Orgeville and Peltier, 2007). In addition, ocean warming may drive the tropical atmospheric circulation (e.g. Tokinaga et al., 2012), whereas atmospheric adjustments may also contribute to ocean warming (e.g. Du and Xie, 2008).

Tropical cyclones (TCs), often accompanied by heavy rainfall, also play a critical role in precipitation changes over coastal regions. It is generally recognized that TC frequency decreases while TC intensity and rainfall increase under global warming (Knutson et al., 2010). In the recent decades, a robust increase over eastern China and a reduction over northern South China Sea have also been

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with aerosol concentration in clouds with low water liquid water content (Rosenfeld et al., 2008; Li et al., 2011), which may propose another possible interpretation for the summer NDSW pattern. 6. Conclusions and further discussions In this article, the regional features of long-term changes in total and extreme precipitations over China are investigated using station-based daily precipitation over eight China domains. The relationships of total and extreme precipitations with large-scale and local atmospheric and oceanic factors are also examined. The results obtained are summarized as follows.

Figure 16. Portrait diagram display of correlations of seasonal precipitation indices (Ptot , P95p , and R95p ) over domains of China with concurrent local factors (Ts, precipitable water, and 500-hPa vertical velocity, being expressed in the plot as T, V, and W, respectively). Each grid square is split by a diagonal in order to show the correlation with respect to both the original (upper left triangle) and the de-trended (lower right triangle) precipitation indices. The values that are statistically significant at the 90, 95, and 99% confidence levels in DJF (MAM, JJA, and SON) are 0.233 (0.231), 0.276 (0.273), and 0.358 (0.354), respectively.

found in the intensity of landfall TCs (Doo-Sun et al., 2014). By separating TC precipitation from monsoon precipitation, Chang et al. (2012) found that the trends of monsoon (i.e. non-TC) precipitation may have been distorted by TCs. That is, TCs may result in an underestimation of the increasing trends and an overestimation of the decreasing trends of monsoon extreme precipitation over China. This feature may be helpful for better understanding the EASM precipitation trends such as the NDSW pattern. Aerosols can influence precipitation mainly through two ways. One is a direct effect by absorbing and scattering solar radiation, and the other is an indirect effect by acting as cloud condensation nuclei (CCN), exerting effects of both increasing and reducing precipitation (Liao et al., 2015). Previous studies have connected precipitation changes to increase in aerosols’ concentration. For example, Zhao et al. (2006) found that an increasing aerosol concentration may contribute greatly to the long-term decreasing trend of precipitation in northern China. Further, Ye et al. (2013) reported a potential linkage between aerosols and the NDSW pattern of summer precipitation trend over China, in spite of a cool zone over southern-central China induced by their cooling effect, which may influence the land–sea temperature contrast and in turn weaken the EASM. It is also reported that precipitation increases with aerosol concentration in deep clouds with adequate water vapour supply, but decreases

(1) The long-term trends of extreme and total precipitations vary greatly with domains and seasons. An obvious wetting tendency appears in winter except in NCH. Meanwhile, extreme precipitation has increased persistently in all regions (Figure 2(a)). In spring, extreme precipitation increases persistently in all domains but SCH, whereas total precipitation increases only in half of the domains (Figure 2(b)). In summer, a typical NDSW pattern is found over eastern China for both extreme and total precipitations (Figure 2(c)). In addition, a significant increase in precipitations is also detected in NWCH. In autumn, negative trends are found in most of the domains except in NWCH and TP for total precipitation, and half of the domains for extreme precipitation (Figure 2(d)). (2) Extreme and total precipitations usually vary with same-sign trends. The extreme precipitation ratio, however, exhibits consistent positive trends in a majority of the cases (over 80%), indicating an increasing importance of extreme precipitation in total precipitation. Extreme precipitation frequency plays a dominant role in determining the extreme precipitation trend compared to precipitation intensity. The dominant role of extreme precipitation in total precipitation trend is mainly found in a few cases with positive total precipitation trends, whereas a negative total precipitation trend generally results from a predominant decrease in non-extreme precipitation. (3) Extreme and total precipitations share similar correlation patterns with large-scale atmospheric and oceanic features in most cases. The long-term changes in total precipitation are strongly linked to the SSTs from the Indian Ocean to the Maritime Continent, the Kuroshio and its extension region, the central-eastern Pacific, and part of the Atlantic Ocean. In summer, the NDSW pattern is closely associated with the weakening of EASM circulation and the increase in Ts of the northern land between 40∘ and 60∘ N. The positive trends of extreme ratio, mainly reflecting the importance of extreme precipitation, are also connected with the SSTs in the regions mentioned above. However, they are mainly contributed by the signals of extreme precipitation, given the opposite contribution from total precipitation in most cases.

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(4) Local warming trends also contribute to the positive trends of extreme ratio in NWCH and BBYR in winter, TP and SWCH in spring, and BBYR in autumn, and to the NDSW pattern of total precipitation in the summer. The contributions of PRW and 𝜔 to the long-term changes in precipitation indices are also discussed, and it is found that PRW and 𝜔 tend to have similar contributions to precipitation in most cases (i.e. PRW and 𝜔 increase or decrease concurrently). In this study, we have revealed the contemporaneous correlation patterns of atmospheric and oceanic features with long-term changes of precipitation indices over China, which is however far from a mechanism attribution. The long-term changes of both extreme and total precipitations over a particular region must be caused by multiple factors. To interpret the mechanisms for these changes, especially the long-term change in extreme precipitation, is largely limited by our current knowledge. For example, the NDSW patter of summer precipitation, in essence, is a result of the weakening of EASM circulation caused by the change in land–sea thermal contrast due to the non-uniform warming between land and oceans. However, as discussed, this pattern is also contributed by TC, aerosol, and others. To what degree these factors play their roles in determining the NDSW pattern is still unclear. An attribution analysis using climate models may be helpful for answering the question in future studies.

Acknowledgements The authors thank Dr. Xiaolan Wang for providing the FORTRAN codes of RHtestsV4 software package, and the four anonymous reviewers who have provided helpful suggestions/comments for improving the overall quality of this paper. This work was supported by the National Natural Science Foundation of China (grants 41275077, 41475069, and 41375081), China LASW State Key Laboratory Special Fund (2013LASW-A05), and the China Special Fund for Meteorological Research in the Public Interest (No. GYHY201406018). We are grateful to the National Meteorological Information Center, the China Meteorological Administration, for providing the newly compiled station-based daily precipitation data set over China.

References Aguilar E, Aziz Barry A, Brunet M, Ekang L, Fernandes A, Massoukina M, Mbah J, Mhanda A, do Nascimento DJ, Peterson TC, Thamba Umba O, Tomou M, Zhang XB. 2009. Changes in temperature and precipitation extremes in western central Africa, Guinea Conakry, and Zimbabwe, 955–2006. J. Geophys. Res. 114: D02115, doi: 10.1029/2008JD011010. Alexander LV, Zhang XB, Peterson TC, Caesar J, Gleason B, Klein Tank AMG, Haylock M, Collins D, Trewin B, Rahimzadeh F, Tagipour A, Rupa Kumar K, Revadekar J, Griffiths G, Vincent L, Stephenson DB, Burn J, Aguilar E, Brunet M, Taylor M, New M, Zhai PM, Rusticucci M, Vazquez-Aguirre JL. 2006. Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111: D05109, doi: 10.1029/2005JD006290.

Berg P, Moseley C, Haerter JO. 2013. Strong increase in convective precipitation in response to higher temperatures. Nat. Geosci. 6: 181–185, doi: 10.1038/ngeo1731. Chang CP, Lei Y, Sui CH, Lin X, Ren F. 2012. Tropical cyclone and extreme rainfall trends in East Asian summer monsoon since mid-20th century. Geophys. Res. Lett. 39: L18702, doi: 10.1029/2012GL052945. Chen Y, Zhai P. 2014a. Two types of typical circulation pattern for persistent extreme precipitation in Central–Eastern China. Q. J. R. Meteorol. Soc. 140: 1467–1478, doi: 10.1002/qj.2231. Chen Y, Zhai P. 2014b. Changing structure of wet periods across southwest China during 1961-2012. Clim. Res. 61: 123–131, doi: 10.3354/cr01247. Collins M, Knutti R, Arblaster J, Dufresne J-L, Fichefet T, Friedlingstein P, Gao X, Gutowski WJ, Johns T, Krinner G, Shongwe M, Tebaldi C, Weaver AJ, Wehner M. 2013. Long-term Climate Change: Projections, Commitments and Irreversibility. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press: Cambridge, UK and New York, NY, 1029–1136. Ding Y, Wang Z, Sun Y. 2008. Inter-decadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I: observed evidences. Int. J. Climatol. 28: 1139–1161, doi: 10.1002/joc.1615. Donat MG, Alexander LV, Yang H, Durre I, Vose R, Dunn RJH, Willett KM, Aguilar E, Brunet M, Caesar J, Hewitson B, Jack C, Klein Tank AMG, Kruger AC, Marengo J, Peterson TC, Renom M, Oria Rojas C, Rusticucci M, Salinger J, Elrayah AS, Sekele SS, Srivastava AK, Trewin B, Villarroel C, Vincent LA, Zhai P, Zhang X, Kitching S. 2013. Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: the HadEX2 dataset. J. Geophys. Res. Atmos. 118: 2098–2118, doi: 10.1002/jgrd.50150. Dong Q, Chen X, Chen T. 2011. Characteristics and changes of extreme precipitation in the Yellow–Huaihe and Yangtze–Huaihe Rivers basins, China. J. Clim. 24: 3781–3795, doi: 10.1175/2010JCLI3653.1. Doo-Sun RP, Chang-Hoi H, Joo-Hong K. 2014. Growing threat of intense tropical cyclones to East Asia over the period 1977–2010. Environ. Res. Lett. 9: 014008, doi: 10.1088/1748-9326/9/1/014008. Du Y, Xie SP. 2008. Role of atmospheric adjustments in the tropical Indian Ocean warming during the 20th century in climate models. Geophys. Res. Lett. 35: L08712, doi: 10.1029/2008GL033631. Fan Y, van den Dool H. 2008. A global monthly land surface air temperature analysis for 1948–present. J. Geophys. Res. Atmos. 113: D01103, doi: 10.1029/2007JD008470. Fan L, Lu C, Yang B, Chen Z. 2012. Long-term trends of precipitation in the North China Plain. J. Geogr. Sci. 22: 989–1001, doi: 10.1007/s11442-012-0978-2. Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Klein Tank AMG, Peterson T. 2002. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19: 193–212. Fu J, Li S. 2013. The influence of regional SSTs on the interdecadal shift of the East Asian summer monsoon. Adv. Atmos. Sci. 30: 330–340, doi: 10.1007/s00376-012-2062-3. Hartmann DL, Klein Tank AMG, Rusticucci M, Alexander LV, Brönnimann S, Charabi Y, Dentener FJ, Dlugokencky EJ, Easterling DR, Kaplan A, Soden BJ, Thorne PW, Wild M, Zhai PM. 2013. Observations: Atmosphere and Surface. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press: Cambridge, UK and New York, NY, 159–254. Haylock MR, Goodess CM. 2004. Interannual variability of European extreme winter rainfall and links with mean large-scale circulation. Int. J. Climatol. 24: 759–776, doi: 10.1002/joc.1033. Haylock M, Nicholls N. 2000. Trends in extreme rainfall indices for an updated high quality data set for Australia, 1910–1998. Int. J. Climatol. 20: 1533–1541, doi: 10.1002/10970088(20001115)20:133.0.CO;2-J. Hu ZZ. 1997. Interdecadal variability of summer climate over East Asia and its association with 500 hPa height and global sea surface temperature. J. Geophys. Res. 102: 19403–19412, doi: 10.1029/97JD01052.

© 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Int. J. Climatol. (2016)

F. WANG AND S. YANG Hu ZZ, Yang S, Wu R. 2003. Long-term climate variations in China and global warming signals. J. Geophys. Res. 108: 4614, doi: 10.1029/2003JD003651. Jiang FQ, Hu RJ, Wang SP, Zhang YW, Tong L. 2013. Trends of precipitation extremes during 1960–2008 in Xinjiang, the Northwest China. Theor. Appl. Climatol. 111: 133–148, doi: 10.1007/s00704-012-0657-3. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D. 1996. The NMC/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77: 437–471. Kendall MR. 1955. Rank Correlation Methods, 2nd edn. Oxford University Press: New York, NY, 196 pp. King AD, Klingaman NP, Alexander LV, Donat MG, Jourdain NC, Maher P. 2014. Extreme rainfall variability in Australia: patterns, drivers, and predictability. J. Clim. 27: 6035–6050, doi: 10.1175/JCLI-D-13-00715.1. Kirtman B, Power SB, Adedoyin AJ, Boer GJ, Bojariu R, Camilloni I, DoblasReyes FJ, Fiore AM, Kimoto M, Meehl GA, Prather M, Sarr A, Schär C, Sutton R, van Oldenborgh GJ, Vecchi G, Wang H-J. 2013. Near-term climate change: projections and predictability. In Climate Change 2013: The Physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel Climate Change, Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press: Cambridge, UK and New York, NY, 953–1028. Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M. 2010. Tropical cyclones and climate change. Nat. Geosci. 3: 157–163, doi: 10.1038/ngeo779. Li QQ, Yang S, Kousky VE, Higgins RW, Lau KM, Xie PP. 2005. Features of cross-Pacific climate shown in the variability of China and U.S. precipitation. Int. J. Climatol. 25: 1675–1696. Li S, Lu J, Huang G, Hu K. 2008. Tropical Indian Ocean basin warming and East Asian summer monsoon: a multiple AGCM study. J. Clim. 21: 6080–6088, doi: 10.1175/2008JCLI2433.1. Li H, Dai A, Zhou T, Lu J. 2010. Responses of East Asian summer monsoon to historical SST and atmospheric forcing during 1950–2000. Clim. Dyn. 34: 501–514, doi: 10.1007/s00382008-0482-7. Li Z, Niu F, Fan J, Liu Y, Rosenfeld D, Ding Y. 2011. Long-term impacts of aerosols on the vertical development of clouds and precipitation. Nat. Geosci. 4: 888–894, doi: 10.1038/ngeo1313. Li J, Dong W, Yan Z. 2012. Changes of climate extremes of temperature and precipitation in summer in eastern China associated with changes in atmospheric circulation in East Asia during 1960–2008. Chin. Sci. Bull. 57: 1856–1861, doi: 10.1007/s11434-012-4989-2. Li YG, He D, Hu JM, Cao J. 2015. Variability of extreme precipitation over Yunnan Province, China 1960–2012. Int. J. Climatol. 35: 245–258, doi: 10.1002/joc.3977. Liao H, Chang W, Yang Y. 2015. Climatic effects of air pollutants over china: a review. Adv. Atmos. Sci. 32: 115–139, doi: 10.1007/s00376-014-0013-x. Liu SC, Fu C, Shiu CJ, Chen JP, Wu F. 2009. Temperature dependence of global precipitation extremes. Geophys. Res. Lett. 36: L17702, doi: 10.1029/2009GL040218. Luo Y, Zhao Z, Ding Y. 2002. Ability of NCAR RegCM2 in reproducing the dominant physical processes during the anomalous rainfall episodes in the summer of 1991 over the Yangtze-Huaihe valley. Adv. Atmos. Sci. 19: 236–254. Ma Z. 2007. The interdecadal trend and shift of dry/wet over the central part of North China and their relationship to the Pacific Decadal Oscillation (PDO). Chin. Sci. Bull. 52: 2130–2139, doi: 10.1007/s11434-007-0284-z. Mann HB. 1945. Non-parametric tests against trend. Econometrica 13: 245–259. Mao R, Gong DY, Yang J, Bao JD. 2011. Linkage between the Arctic Oscillation and winter extreme precipitation over central-southern China. Clim. Res. 50: 187–201, doi: 10.3354/cr01041. New M, Hulme M, Jones P. 2000. Representing twentieth-century space–time Climate variability. Part II: development of 1901–96 monthly grids of terrestrial surface climate. J. Clim. 13: 2217–2238, doi: 10.1175/1520-0442(2000)0132.0.CO;2. O’Gorman PA, Schneider T. 2009. The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc. Natl. Acad. Sci. 106: 14773–14777, doi: 10.1073/pnas.0907610106.

d’Orgeville M, Peltier WR. 2007. On the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation: might they be related? Geophys. Res. Lett. 34: L23705, doi: 10.1029/2007GL031584. Pryor SC, Howe JA, Kunkel KE. 2009. How spatially coherent and statistically robust are temporal changes in extreme precipitation in the contiguous USA? Int. J. Climatol. 29: 31–45, doi: 10.1002/joc.1696. Qian W, Lin X. 2005. Regional trends in recent precipitation indices in China. Meteorog. Atmos. Phys. 90: 193–207, doi: 10.1007/s00703-004-0101-z. Qian W, Qin A. 2008. Precipitation division and climate shift in China from 1960 to 2000. Theor. Appl. Climatol. 93: 1–17, doi: 10.1007/s00704-007-0330-4. Qian C, Zhou T. 2014. Multidecadal variability of North China aridity and its relationship to PDO during 1900–2010. J. Clim. 27: 1210–1222, doi: 10.1175/JCLI-D-13-00235.1. Qian W, Fu J, Yan Z. 2007. Decrease of light rain events in summer associated with a warming environment in China during 1961–2005. Geophys. Res. Lett. 34: L11705, doi: 10.1029/2007 GL029631. Qian C, Yu JY, Chen G. 2014. Decadal summer drought frequency in China: the increasing influence of the Atlantic Multi-decadal Oscillation. Environ. Res. Lett. 9: 124004, doi: 10.1088/1748-9326/9/12/124004. Ren ZH, Yu Y, Zou FL, Xu Y. 2012. Quality detection of surface historical basic meteorological data. J. Appl. Meteorol. Sci. 23: 739–747 (In Chinese). Rosenfeld D, Lohmann U, Raga GB, O’Dowd CD, Kulmala M, Fuzzi S, Reissell A, Andreae MO. 2008. Flood or drought: how do aerosols affect precipitation? Science 321: 1309–1313, doi: 10.1126/science.1160606. Shi Y, Shen Y, Kang E, Li D, Ding Y, Zhang G, Hu R. 2007. Recent and future climate change in Northwest China. Clim. Change 80: 379–393, doi: 10.1007/s10584-006-9121-7. Shiu CJ, Liu SC, Fu C, Dai A, Sun Y. 2012. How much do precipitation extremes change in a warming climate? Geophys. Res. Lett. 39: L17707, doi: 10.1029/2012GL052762. Skansi MM, Brunet M, Sigró J, Aguilar E, Arevalo Groening JA, Bentancur OJ, Castellón Geier YR, Correa Amaya RL, Jácome H, Malheiros Ramos A, Oria Rojas C, Pasten AM, Sallons Mitro S, Villaroel Jiménez C, Martínez R, Alexander LV, Jones PD. 2013. Warming and wetting signals emerging from analysis of changes in climate extreme indices over South America. Glob. Planet. Change 100: 295–307, doi: 10.1016/j.gloplacha.2012.11.004. Smith TM, Reynolds RW, Peterson TC, Lawrimore J. 2008. Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J. Clim. 21: 2283–2296, doi: 10.1175/2007JCLI2100.1. Tian B, Fan K. 2013. Factors favorable to frequent extreme precipitation in the upper Yangtze River Valley. Meteorog. Atmos. Phys. 121: 189–197, doi: 10.1007/s00703-013-0261-9. Tokinaga H, Xie SP, Deser C, Kosaka Y, Okumura YM. 2012. Slowdown of the Walker circulation driven by tropical Indo-Pacific warming. Nature 491: 439–443, doi: 10.1038/nature11576. Utsumi N, Seto S, Kanae S, Maeda EE, Oki T. 2011. Does higher surface temperature intensify extreme precipitation? Geophys. Res. Lett. 38: L16708, doi: 10.1029/2011GL048426. Villarini G, Smith JA, Vecchi GA. 2013. Changing frequency of heavy rainfall over the central United States. J. Clim. 26: 351–357, doi: 10.1175/JCLI-D-12-00043.1. Wang XL, Swail VR. 2001. Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Clim. 14: 2204–2221, doi: 10.1175/1520-0442(2001)0142.0.CO;2. Wang Y, Yan Z. 2011. Changes of frequency of summer precipitation extremes over the Yangtze River in association with large-scale oceanic-atmospheric conditions. Adv. Atmos. Sci. 28: 1118–1128, doi: 10.1007/s00376-010-0128-7. Wang Y, Zhou L. 2005. Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophys. Res. Lett. 32: L09707, doi: 10.1029/2005GL022574. Wang XL, Chen HF, Wu YH, Feng Y, Pu Q. 2010. New techniques for the detection and adjustment of shifts in daily precipitation data series. J. Appl. Meteorol. Climatol. 49: 2416–2436, doi: 10.1175/2010JAMC2376.1. Wang HJ, Sun JQ, Chen HP, Zhu YL, Zhang Y, Jiang DB, Lang XM, Fan K, Yu ET, Yang S. 2012. Extreme climate in China: facts, simulation and projection. Meteorol. Z. 21: 279–304.

© 2016 The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.

Int. J. Climatol. (2016)

REGIONAL CHINESE EXTREME PRECIPITATION AND ATMOSPHERIC–OCEANIC FACTORS Wang B, Zhang M, Wei J, Wang S, Li X, Li S, Zhao A, Li X, Fan J. 2013. Changes in extreme precipitation over Northeast China, 1960–2011. Quat. Int. 298: 177–186, doi: 10.1016/j.quaint.2013.01.025. Wang F, Yang S, Higgins W, Li Q, Zuo Z. 2014. Long-term changes in total and extreme precipitation over China and the United States and their links to oceanic–atmospheric features. Int. J. Climatol. 34: 286–302, doi: 10.1002/joc.3685. Wu F, Fu C. 2013. Change of precipitation intensity spectra at different spatial scales under warming conditions. Chin. Sci. Bull. 58: 1385–1394, doi: 10.1007/s11434-013-5699-0. Wu L, Cai W, Zhang L, Nakamura H, Timmermann A, Joyce T, McPhaden MJ, Alexander M, Qiu B, Visbeck M, Chang P, Giese B. 2012. Enhanced warming over the global subtropical western boundary currents. Nat. Clim. Change 2: 161–166, doi: 10.1038/nclimate1353. Xiao M, Zhang Q, Singh VP. 2015. Influences of ENSO, NAO, IOD and PDO on seasonal precipitation regimes in the Yangtze River basin, China. Int. J. Climatol. 35: 3556–3567, doi: 10.1002/joc.4228. Yang F, Lau KM. 2004. Trend and variability of China precipitation in spring and summer: linkage to sea-surface temperatures. Int. J. Climatol. 24: 1625–1644, doi: 10.1002/joc.1094. Yao C, Yang S, Qian WH, Lin Z, Wen M. 2008. Regional summer precipitation events in Asia and their changes in the past decades. J. Geophys. Res. 113: D17107, doi: 10.1029/2007JD009603. Ye J, Li W, Li L, Zhang F. 2013. “North drying and south wetting” summer precipitation trend over China and its potential linkage with aerosol loading. Atmos. Res. 125–126: 12–19, doi: 10.1016/j.atmosres.2013.01.007. You QL, Kang SC, Aguilar E, Pepin N, Flügel WA, Yan YP, Xu

YW, Zhang YJ, Huang J. 2011. Changes in daily climate extremes in China and their connection to the large scale atmospheric circulation during 1961–2003. Clim. Dyn. 36: 2399–2417, doi: 10.1007/s00382-009-0735-0. Yu L, Furevik T, Otterå O, Gao Y. 2015. Modulation of the Pacific Decadal Oscillation on the summer precipitation over East China: a comparison of observations to 600-years control run of Bergen Climate Model. Clim. Dyn. 44: 475–494, doi: 10.1007/s00382-014-2141-5. Zhai P, Sun A, Ren F, Liu X, Gao B, Zhang Q. 1999. Changes of climate extremes in China. Clim. Change 42: 203–218, doi: 10.1023/A:1005428602279. Zhai P, Zhang X, Wan H, Pan X. 2005. Trends in total precipitation and frequency of daily precipitation extremes over China. J. Clim. 18: 1096–1108, doi: 10.1175/JCLI-3318.1. Zhang Q, Singh VP, Li J, Jiang F, Bai Y. 2012. Spatio-temporal variations of precipitation extremes in Xinjiang, China. J. Hydrol. 434–435: 7–18, doi: 10.1016/j.jhydrol.2012.02.038. Zhao C, Tie X, Lin Y. 2006. A possible positive feedback of reduction of precipitation and increase in aerosols over eastern central China. Geophys. Res. Lett. 33: L11814, doi: 10.1029/2006GL025959. Zhou T, Gong D, Li J, Li B. 2009. Detecting and understanding the multi-decadal variability of the East Asian Summer Monsoon-Recent progress and state of affairs. Meteorol. Z. 18: 455–467, doi: 10.1127/0941-2948/2009/0396. Zhu C, Wang B, Qian W, Zhang B. 2012. Recent weakening of northern East Asian summer monsoon: a possible response to global warming. Geophys. Res. Lett. 39: L09701, doi: 10.1029/ 2012GL051155.

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Int. J. Climatol. (2016)