未来30年亚洲降水情景预估及偏差订正

杨阳; 戴新刚; 汪萍

doi:10.3878/j.issn.1006-9895.2104.20241

未来30年亚洲降水情景预估及偏差订正

doi: 10.3878/j.issn.1006-9895.2104.20241

杨阳^{1, 2, 4,},
戴新刚^1, ,,
汪萍³

1.
中国科学院东亚区域气候与环境重点实验室，大气物理研究所，北京100029
2.
中山大学大气科学学院，珠海519082
3.
中国气象科学研究院大气成分与环境气象研究所，北京100081
4.
兰州大学大气科学学院，兰州730000

基金项目: 国家自然科学基金项目41675087、42061144015，国家重点研发计划项目2016YFA0600404、2016YFA0601901

详细信息

作者简介:
杨阳，女，博士研究生，主要从事气候变化研究。E-mail: yangy2016@lzu.edu.cn

通讯作者:
戴新刚，E-mail: daixg@mail.iap.ac.cn

中图分类号: P467
计量
- 文章访问数: 659
- HTML全文浏览量: 103
- PDF下载量: 162
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-04
- 录用日期: 2021-06-15
- 网络出版日期: 2021-06-15
- 刊出日期: 2022-01-18

Projection of Asian Precipitation for the Coming 30 Years and Its Bias Correction

Yang YANG^{1, 2, 4
,},
Xingang DAI^{1
, ,},
Ping WANG³

1.
Key Laboratory of Regional Climate–Environment for Temperate East Asia , Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
2.
School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082
3.
Key Laboratory for Atmospheric Chemistry, Institute of Atmospheric Composition and Environment Meteorology, Chinese Academy of Meteorological Sciences, Beijing, 100081
4.
College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000

Funds: National Natural Science Foundation of China (Grants 41675087, 42061144015), National Key Research and Development Program of China (Grants 2016YFA0600404, 2016YFA0601901)

摘要

摘要: 借助第五次国际耦合模式比较计划（Coupled Model Intercomparison Project Phase 5, CMIP5）多模式集合数据及英国气候研究所（Climatic Research Unit Time-Series version 4.0, CRU TSv4.0）的格点降水资料，分析了多模式集合平均降水在亚洲的偏差分布特征，检验了三种偏差订正统计方法，并且预估了2021～2050年亚洲降水的可能变化。结果表明，在CMIP5历史气候模拟中，多模式集合降水在亚洲存在明显偏差，北方降水偏多，南方偏少，其中在青藏高原、内蒙古、蒙古国等地明显偏多达30%～40%，南亚偏少30%～40%，在越南和华南沿海偏少20%～30%等。2006～2015年预估降水偏差型与历史气候模拟相似，具有准定常性，可以通过二者之差将其消去。偏差订正检验表明，单纯除去模式气候漂移后的降水距平太小，尽管距平符号一致率较高。在暖季（5～10月），一元对数回归偏差订正结果在北方略优于一元差分回归，在冷季（11月至次年4月）与此相反，二者结合可以构成区域组合回归偏差订正法。最后，用组合订正法订正了RCP4.5情景下20个CMIP5模式集合2021～2050年亚洲降水预估偏差，又利用某些区域的去除模式漂移后的订正结果对其盲区进行了补充订正。结果表明，相对于1976～2005年气候平均，在暖季，中国南方、南亚东北部、中亚南部、阿拉伯半岛东北部等地降水可能减少10%～20%；从中国的三江源区到淮河流域带降水会增加约20%，东北南部的降水会增加约10%；新疆北部降水增加约10%，南部约20%；华北和东北大部降水减少约10%～20%；中南半岛北部降水增加约10%；亚洲高纬度地带降水也略有增加。在冷季，亚洲降水呈现北方增加，南方减少的格局，其中南亚降水减少最明显，达−10%左右，中国西南部减少约−5%；中国西部降水增加幅度为20%～40%，华北和东北增加约5%；亚洲高纬度降水增加约为10%～40%。因此，随着气候暖化，未来30年中国的淮河流域、长江和黄河上游可能降水增多，而西南地区的旱情可能会持续，建议有关部门提前做好应对部署。
- 亚洲季风 /
- 降水预估 /
- CMIP5 /
- 偏差订正 /
- 旱涝趋势
Abstract: This study focuses on the bias of the multimodel ensemble mean in the precipitation simulated using models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) compared with the precipitation data using Climatic Research Unit Timeseries version 4.0 (CRU TS v4.0). Three bias correction methods are tested, and a precipitation projection with the correction is made for the coming 30 years (2021–2050) based on the selected 20 CMIP5 models. Results show that the precipitation in the CMIP5 historical simulation is overestimated in northern Asia and underestimated in the south for 1960–2005 with 30%–40% more precipitation than that observed in the Tibetan Plateau, Inner Mongolia, and Mongolia; 20%–30% less in the South China Coast and Vietnam; and 30%–40% less in South Asia than that of the observation. The bias pattern of the projected precipitation for 2006–2015 under the Representative Concentration Pathway (RCP) 4.5 scenario is found to be similar to that from the CMIP5 historical climate simulation, implying that the bias pattern is almost stationary and should belong to the model climate drift that can be removed using the difference between the period-mean projection and historical simulation. However, this bias correction leads to a much small magnitude of the precipitation anomaly, although it has a good anomaly rate compared with the observation. The bias correction test confirms that the performance of the bias correction using logarithm regression (LR) is better in northern Asia compared with the year-to-year increment regression (YYIR) during the warm season (May–October). Meanwhile, the YYIR is better than the LR in southern Asia in this season. Nevertheless, the LR is better in the south, and YYIR is better in the north during the cold season (November throughout next April). Therefore, combining the two regression methods can form a regional combination bias correction. The regional combination method is applied in the bias correction for the 2021–2050 precipitation projection of the Asian continent under the RCP4.5 scenario, in which an additional bias correction of climate drift removal is added in the blind areas in the two bias corrections. The projection for the warm season shows more or less changes in the precipitation pattern compared with that of 1976–2005, such as the 10%–20% decrease in precipitation in the Southern China, the northeastern part of South Asia, south part of Central Asia, and northeastern Arabian Peninsula. The projected precipitation would increase to approximately 20% in the belt from the Three-River-Source area throughout the Huaihe delta area, 10% increase in the southern part of Northeast China, 10% and 20% increase in northern and southern Xinjiang, respectively, 10% or 20% decrease in North China and most of Northeast China, and 10% increase in northern Indo–China Peninsula, in addition to a minor increase in precipitation in the high latitude of Asia. In the cold season, the projected precipitation would increase in the north and decrease in the south of Asia, such as the 10% decrease in South Asia, 5% decrease in Southwest China, 20%–40% increase in West China, 5% increase in North and Northeast China, and 10%–40% increase in the high latitude of Asia. Consequently, there would be more precipitation with potential floods in the upper reaches of the Yangtze River and Yellow River over the next 30 years, whereas the drought would possibly continue in Southwest China as it has experienced for the last decade. These will provide suggestions for the relevant department of the local government to take advance measures concerning the risks of flood and drought in the context of climate warming.
- Asian monsoon /
- Precipitation projection /
- CMIP5 /
- Bias correction /
- Drying and flooding

HTML全文

图 1 1960～2005年亚洲气候平均年降水量（单位：mm）：(a) 20个CMIP5 模式集合模拟；（b）观测（CRU TS v4.0）

Figure 1. Climate mean annual precipitation (units: mm) from 1960 to 2005: (a) Climate mean simulated using 20 CMIP5 models; (b) observations (CRU TS v4.0)

下载: 全尺寸图片幻灯片

图 2 1960～2005年20个CMIP5模式集合平均亚洲降水的偏差百分率：（a）暖季（5～10月）降水；（b）冷季（11月至次年4月）降水

Figure 2. Bias percentage of Asian precipitation between the ensemble mean of 20 CMIP5 models during 1960–2005: (a) Warm season (May–October); (b) cold season (November throughout next April)

下载: 全尺寸图片幻灯片

图 3 RCP4.5情景下2006～2015年20个CMIP5模式集合平均与观测（CRU TS v4.0）亚洲降水的偏差百分率：（a）暖季；（b）冷季

Figure 3. Bias percentage of Asian precipitation between the ensemble mean of 20 CMIP5 models and the observation (CRU TS v 4.0) during 2006–2015 under the RCP4.5 scenario: (a) Warm season; (b) cold season

下载: 全尺寸图片幻灯片

图 4 RCP4.5情景下2006～2015年20个CMIP5模式集合预估降水相对于1976～2005年历史气候模拟降水的距平百分率（上）及其与观测降水（CRU TS v4.0）的距平符号一致格点（“+”）分布（下）：（a、c）暖季；（b、d）冷季

Figure 4. Percentage of the precipitation anomaly projected using 20 CMIP5 models ensemble mean vs the counterpart of its historical simulations for 1976–2005 (upper panel) and distributions of grid points with the same sign (marked as “+”) in the precipitation anomaly with the observation (CRU TS v4.0，lower panel) in Asia during 2006–2015 under the RCP4.5 scenario: (a, c) Warm season; (b, d) cold season

下载: 全尺寸图片幻灯片

图 5 2006～2015年亚洲观测（CRU TS v4.0）降水距平百分率：（a）暖季；（b）冷季。参考态：1976～2005年

Figure 5. Percentage of precipitation anomaly in Asia from CRU TS v4.0 for 2006–2015: (a) Warm season; (b) cold season. Reference years: 1976–2005

下载: 全尺寸图片幻灯片

图 6 RCP4.5情景下2006～2015年线性回归订正后亚洲暖季模式预估降水与观测降水距平百分率（上）及其距平同号格点分布（下）：（a、c）一元对数回归；（b、d）一元年际增量回归。参考态：1976～2005年

Figure 6. Anomaly percentage of the warm season precipitation projected using 20 CMIP5 model ensemble mean under RCP4.5 for 2006–2015 (upper) and the corresponding grid distribution with the correct sign (“+”) with the observation precipitation anomaly (lower): (a, c) Logarithmic regression; (b, d) year-to-year increment regression. Reference years: 1976–2005

下载: 全尺寸图片幻灯片

图 7 同图6，但为冷季降水

Figure 7. Same as Fig. 6, but for cold season precipitation

下载: 全尺寸图片幻灯片

图 8 在RCP4.5情景下20个CMIP5模式集合预估并进行组合回归订正后2006～2015年亚洲降水距平百分率: （a）在暖季30°N以北用一元对数回归订正，以南用一元年际增量回归订正；（b）冷季与暖季相反。参考态：1976～2005年

Figure 8. Asian precipitation anomaly percentage projected using CMIP5 20 model ensemble mean under the RCP4.5 scenario with bias correction for 2006–2015 versus that of the 1976–2005 observation: (a) Logarithm regression in the north of 30°N with year-to-year increment regression in the south for the warm seasons; (b) opposite combination in the regressions for the cold seasons

下载: 全尺寸图片幻灯片

图 9 RCP4.5情景下经偏差订正后20个CMIP5模式集合预估的2021～2050年亚洲年暖季（左）和冷季（右）降水距平百分率：（a）一元年际增量回归订正；（b）组合回归订正即一元年际增量回归（30ºN以北）和一元对数回归（30°N以南）；（c，d）去除模式气候漂移后暖季和冷季降水距平百分率。图a和b的参考态：1976～2005年观测降水；图c和d的参考态：1976～2005年 CMIP5历史模拟降水

Figure 9. Bias-corrected precipitation anomaly percentage projected using CMIP5 20 model ensemble mean under RCP4.5 scenario for 2021–2050 in the warm season (left) and cold season (right): (a) Year-to-year increment regression; (b) year-to-year increment regression in the north of 30°N and logarithm in the south; (c, d) model drift removed for warm and cold seasons, respectively. Figures a and b are with reference to the 1976–2005 observation; figures c and d are with reference to the precipitation of the CMIP5 historical climate simulation for 1976–2005

下载: 全尺寸图片幻灯片

表 1 20个CMIP5模式参数信息

Table 1. Parameters of the 20 CMIP5 models

模式名称	单位及国家	格点数（纬向×经向）	模式名称	单位及国家	格点数（纬向×经向）
ACCESS1-0	CSIRO-BOM, 澳大利亚	192×145	GISS-E2-R	NASA GISS, 美国	144×90
ACCESS1-3	CSIRO-BOM, 澳大利亚	192×145	GISS-E2-R-CC	NASA GISS, 美国	144×90
BCC-CSM1-1	BCC, 中国	128×64	INMCM4	INM, 俄罗斯	180×120
BCC-CSM1-1-m	BCC, 中国	320×160	IPSL-CM5A-LR	IPSL, 法国	96×96
CanESM2	CCCMA, 加拿大	128×64	IPSL-CM5A-MR	IPSL, 法国	144×143
CCSM4	NCAR, 美国	288×192	IPSL-CM5B-LR	IPSL, 法国	96×96
CNRM-CM5	CNRM-CERFACS, 法国	256×128	MPI-ESM-LR	MPI-M, 德国	192×96
CSIRO-Mk3-6-0	CSIRO-QCCCE, 澳大利亚	192×96	MPI-ESM-MR	MPI-M, 德国	192×96
GISS-E2-H	NASA GISS, 美国	144×90	NorESM1-M	NCC, 挪威	144×96
GISS-E2-H-CC	NASA GISS, 美国	144×90	NorESM1-ME	NCC, 挪威	144×96

下载: 导出CSV

参考文献(73)

[1]	Ashok K, Behera S K, Rao S A, et al. 2007. El Niño Modoki and its possible teleconnection [J]. J. Geophy. Res. Earth Surface, 112(C11): C11007. doi: 10.1029/2006JC003798
[2]	Cai W J, Yang K, Wu L X, et al. 2021. Opposite response of strong and moderate positive Indian Ocean Dipole to global warming [J]. Nature Climate Change, 11: 27−32. doi: 10.1038/s41558-020-00943-1
[3]	Chang C P, Johnson R H, Ha K J, et al. 2018. The multiscale global monsoon system: Research and prediction challenges in weather and climate [J]. Bull. Amer. Meteor. Soc., 99(9): ES149−ES153. doi: 10.1175/BAMS-D-18-0085.1
[4]	Chen W, Feng J, Wu R G. 2013. Roles of ENSO and PDO in the link of the East Asian winter monsoon to the following summer monsoon [J]. J. Climate, 26(2): 622−635. doi: 10.1175/JCLI-D-12-00021.1
[5]	Chen Z M, Zhou T J, Zhang L X, et al. 2020. Global land monsoon precipitation changes in CMIP6 projections [J]. Geophys. Res. Lett., 47(14): e2019GL086902. doi: 10.1029/2019GL086902
[6]	Cook B I, Mankin J S, Marvel K, et al. 2020. Twenty-first century drought projections in the CMIP6 forcing scenarios [J]. Earth's Future, 8(6): e2019EF001461. doi: 10.1029/2019EF001461
[7]	Dai X G, Wang P. 2017. A new classification of large-scale climate regimes around the Tibetan Plateau based on seasonal circulation patterns [J]. Advances in Climate Change Research, 8(1): 26−36. doi: 10.1016/j.accre.2017.01.001
[8]	Dai X G, Wang P. 2018. Identifying the early 2000s hiatus associated with internal climate variability [J]. Scientific Reports, 8: 13602. doi: 10.1038/s41598-018-31862-z
[9]	戴新刚, 汪萍. 2020. 亚洲中部干旱气候研究综述与机理分析 [J]. 沙漠与绿洲气象, 14(1): 1−12. doi: 10.12057/j.issn.1002-0799.2020.01.001 Dai X G, Wang P. 2020. A review of aridity studies for Central Asia with mechanism analysis [J]. Desert and Oasis Meteorology (in Chinese), 14(1): 1−12. doi: 10.12057/j.issn.1002-0799.2020.01.001
[10]	Dai X G, Wang P, Chou J F. 2003. Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China [J]. Chinese Science Bulletin, 48(24): 2730−2734. doi: 10.1007/BF02901765
[11]	Dai X G, Wang P, Wu G X, et al. 2004a. Teleconnection between Indian monsoon and East Asian circulation [J]. Acta Meteorologica Sinica, 18(4): 397−410.
[12]	Dai X G, Wang P, Zhang P Q, et al. 2004b. Rainfall spectrum change in North China and its possible mechanism [J]. Progress in Natural Science, 14(7): 598−604. doi: 10.1080/10020070412331344011
[13]	Dai X G, Fu C B, Wang P. 2005. Interdecadal change of atmospheric stationary waves and North China drought [J]. Chinese Physics, 14(4): 850−858. doi: 10.1088/1009-1963/14/4/038
[14]	Dai A G, Fyfe J C, Xie S P, et al. 2015a. Decadal modulation of global surface temperature by internal climate variability [J]. Nature Climate Change, 5(6): 555−559. doi: 10.1038/nclimate2605
[15]	Dai X G, Liu Y, Wang P. 2015b. Warm-dry collocation of recent drought in southwestern China tied to moisture transport and climate warming [J]. Chin. Phys. B, 24(4): 049201. doi: 10.1088/1674-1056/24/4/049201
[16]	丁一汇. 1993.1991年江淮流域持续性特大暴雨研究[M]. 北京: 气象出版社, 1–253 Ding Y H. 1993. A Study of Sustained Heavy Rainfall in the Yangtze–Huai River Valleys in 1991 (in Chinese) [M]. Beijing: China Meteorological Press, 1–253.
[17]	Ding Y H, Chan J C L. 2005. The East Asian summer monsoon: An overview [J]. Meteor. Atmos. Phys., 89: 117−142. doi: 10.1007/s00703-005-0125-z
[18]	Dong B, Dai A G. 2015. The influence of the interdecadal Pacific oscillation on temperature and precipitation over the globe [J]. Clim. Dyn., 45(9): 2667−2681. doi: 10.1007/s00382-015-2500-x
[19]	Dong B, Dai A G, Vuille M, et al. 2018. Asymmetric modulation of ENSO teleconnections by the interdecadal Pacific oscillation [J]. J. Climate, 31(18): 7337−7361. doi: 10.1175/JCLI-D-17-0663.1
[20]	Duan Y W, Wu P L, Chen X L, et al. 2018. Assessing global warming induced changes in summer rainfall variability over eastern China using the latest Hadley Centre Climate Model HadGEM3-GC2 [J]. Advances in Atmospheric Sciences, 35(8): 1077−1093. doi: 10.1007/s00376-018-7264-x
[21]	Eyring V, Bony S, Meehl G A, et al. 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization [J]. Geoscientific Model Development, 9(5): 1937−1958. doi: 10.5194/gmd-9-1937-2016
[22]	Fan K, Lin M J, Gao Y Z. 2009. Forecasting the summer rainfall in North China using the year-to-year increment approach [J]. Sci. China Ser. D Earth Sci., 52(4): 532−539. doi: 10.1007/s11430-009-0040-0
[23]	Fasullo J, Webster P J. 2002. Hydrological signatures relating the Asian summer monsoon and ENSO [J]. J. Climate, 15(21): 3082−3095. doi:10.1175/1520-0442(2002)015<3082:HSRTAS>2.0.CO;2
[24]	Feng J, Wang L, Chen W. 2014. How does the East Asian summer monsoon behave in the decaying phase of El Niño during different PDO phases? [J]. J. Climate, 27(7): 2682−2698. doi: 10.1175/JCLI-D-13-00015.1
[25]	符淙斌. 1978. 用赤道太平洋某些海洋气象要素做副热带高压长期预报的试验 [J]. 气象, 4(2): 16−17. doi: 10.7519/j.issn.1000-0526.1978.02.011 Fu C B. 1978. Test on long-term prediction of the subtropical high basing on the oceanic and meteorological factors in Equatorial Pacific [J]. J. Meteor. (in Chinese), 4(2): 16−17. doi: 10.7519/j.issn.1000-0526.1978.02.011
[26]	Goswami B N, Madhusoodanan M S, Neema C P, et al. 2006. A physical mechanism for North Atlantic SST influence on the Indian summer monsoon [J]. Geophys. Res. Lett., 33(2): L02706. doi: 10.1029/2005GL024803
[27]	Harris I, Osborn T J, Jones P, et al. 2020. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset [J]. Scientific Data, 7: 109. doi: 10.1038/s41597-020-0453-3
[28]	黄荣辉, 陈文, 丁一汇, 等. 2003. 关于季风动力学以及季风与ENSO循环相互作用的研究 [J]. 大气科学, 27(4): 484−502. doi: 10.3878/j.issn.1006-9895.2003.04.05 Huang R H, Chen W, Ding Y H, et al. 2003. Studies on the monsoon dynamics and the interaction between monsoon and ENSO cycle [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 27(4): 484−502. doi: 10.3878/j.issn.1006-9895.2003.04.05
[29]	Huang X, Zhou T J, Dai A G, et al. 2020a. South Asian summer monsoon projections constrained by the interdecadal Pacific oscillation [J]. Science Advances, 6(11): eaay6546. doi: 10.1126/sciadv.aay6546
[30]	Huang X, Zhou T J, Turner A, et al. 2020b. The recent decline and recovery of Indian summer monsoon rainfall: Relative roles of external forcing and internal variability [J]. J. Climate, 33(12): 5035−5060. doi: 10.1175/JCLI-D-19-0833.1
[31]	IPCC. 2013. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [M]. Cambridge: Cambridge University Press, 1535pp.
[32]	Jiang J, Zhou T J, Chen X L, et al. 2020. Future changes in precipitation over Central Asia based on CMIP6 projections [J]. Environ. Res. Lett., 15(5): 054009. doi: 10.1088/1748-9326/ab7d03
[33]	Krishnamurthy L, Krishnamurthy V. 2014. Influence of PDO on South Asian summer monsoon and monsoon–ENSO relation [J]. Clim. Dyn., 42(9): 2397−2410. doi: 10.1007/s00382-013-1856-z
[34]	Krishnamurthy L, Krishnamurthy V. 2016. Teleconnections of Indian monsoon rainfall with AMO and Atlantic tripole [J]. Clim. Dyn., 46(7): 2269−2285. doi: 10.1007/s00382-015-2701-3
[35]	Krishnamurthy V, Goswami B N. 2000. Indian monsoon–ENSO relationship on interdecadal timescale [J]. J. Climate, 13(3): 579−595. doi:10.1175/1520-0442(2000)013<0579:IMEROI>2.0.CO;2
[36]	Kumar A, Yadav J, Mohan R. 2020. Global warming leading to alarming recession of the Arctic sea-ice cover: Insights from remote sensing observations and model reanalysis [J]. Heliyon, 6(7): e04355. doi: 10.1016/j.heliyon.2020.e04355
[37]	Laliberté F, Zika J, Mudryk L, et al. 2015. Constrained work output of the moist atmospheric heat engine in a warming climate [J]. Science, 347(6221): 540−543. doi: 10.1126/science.1257103
[38]	Lee J Y, Wang B. 2014. Future change of global monsoon in the CMIP5 [J]. Climate Dyn. , 42(1–2): 101–119. doi: 10.1007/s00382-012-1564-0
[39]	Li J, Zhu Z W, Dong W J. 2017. A new mean-extreme vector for the trends of temperature and precipitation over China during 1960–2013 [J]. Meteor. Atmos. Phys., 129(3): 273−282. doi: 10.1007/s00703-016-0464-y
[40]	Li D H, Zhou T J, Zhang W X. 2019. Extreme precipitation over East Asia under 1.5°C and 2°C global warming targets: A comparison of stabilized and overshoot projections [J]. Environ. Res. Commun., 1(8): 085002. doi: 10.1088/2515-7620/ab3971
[41]	Li J, Wang B, Yang Y M. 2020. Diagnostic metrics for evaluating model simulations of the East Asian monsoon [J]. J. Climate, 33(5): 1777−1801. doi: 10.1175/JCLI-D-18-0808.1
[42]	Lu R Y, Hina S, Hong X W, et al. 2020. Upper- and lower-tropospheric circulation anomalies associated with interannual variation of Pakistan rainfall during summer [J]. Adv. Atmos. Sci., 37(11): 1179−1190. doi: 10.1007/s00376-020-0137-0
[43]	Naidu P D, Ganeshram R, Bollasina M A, et al. 2020. Coherent response of the Indian monsoon rainfall to Atlantic multi-decadal variability over the last 2000 years [J]. Sci. Rep., 10: 1302. doi: 10.1038/s41598-020-58265-3
[44]	Revadekar J V, Preethi B. 2012. Statistical analysis of the relationship between summer monsoon precipitation extremes and food grain yield over India [J]. Int. J. Climatol., 32(3): 419−429. doi: 10.1002/joc.2282
[45]	Seidel D J, Fu Q, Randel W J, et al. 2008. Widening of the tropical belt in a changing climate [J]. Nat. Geosci., 1: 21−24. doi: 10.1038/ngeo.2007.38
[46]	Sooraj K P, Terray P, Mujumdar M. 2015. Global warming and the weakening of the Asian summer monsoon circulation: Assessments from the CMIP5 models [J]. Clim. Dyn., 45: 233−252. doi: 10.1007/s00382-014-2257-7
[47]	陶诗言. 1980. 中国之暴雨[M]. 北京: 科学出版社, 225pp Tao S Y. 1980. Rainstorms in China (in Chinese) [M]. Beijing: Science Press, 225pp.
[48]	陶诗言, 倪允琪, 赵思雄, 等. 2001.1998年夏季中国暴雨的形成机理与预报研究[M]. 北京: 气象出版社, 184pp Tao S Y, Ni Y Q, Zhao S X, et al. 2001. Study on the Formation Mechanism and Forecast of Chinese Summer Rainfall in 1998 (in Chinese) [M]. Beijing: China Meteorological Press, 184pp.
[49]	Taylor K E, Stouffer R J, Meehl G A. 2012. An overview of CMIP5 and the experiment design [J]. Bull. Amer. Meteor. Soc., 93(4): 485−498. doi: 10.1175/BAMS-D-11-00094.1
[50]	Terjung W H, Mearns L O, Todhunter P E, et al. 1989. Effects of monsoonal fluctuations on grains in China. Part II: Crop water requirements [J]. J. Climate, 2(1): 19−37. doi:10.1175/1520-0442(1989)002<0019:EOMFOG>2.0.CO;2
[51]	Wang B. 2006. The Asian Monsoon [M]. New York: Springer, 843pp.
[52]	Wang L, Chen W, Huang R H. 2008. Interdecadal modulation of PDO on the impact of ENSO on the East Asian winter monsoon [J]. Geophys. Res. Lett., 35(20): L20702. doi: 10.1029/2008GL035287
[53]	Wang B, Liu J, Kim H J, et al. 2013. Northern Hemisphere summer monsoon intensified by Mega-El Niño/Southern Oscillation and Atlantic multidecadal Oscillation [J]. Proc Natl Acad Sci USA, 110(14): 5347−5352. doi: 10.1073/pnas.1219405110
[54]	Wang S S, Huang J P, He Y L, et al. 2014. Combined effects of the Pacific decadal oscillation and El Niño-Southern oscillation on global land Dry–Wet Changes [J]. Scientific Report, 4: 6651. doi: 10.1038/srep06651
[55]	Wang B, Xiang B Q, Li J, et al. 2015. Rethinking Indian monsoon rainfall prediction in the context of recent global warming [J]. Nature Communication, 6: 7154. doi: 10.1038/ncomms8154
[56]	Wang B, Jin C H, Liu J. 2020. Understanding future change of global monsoons projected by CMIP6 models [J]. J. Climate, 33(15): 6471−6489. doi: 10.1175/JCLI-D-19-0993.1
[57]	王会军, 任宏利, 陈活泼, 等. 2020. 中国气候预测研究与业务发展的回顾 [J]. 气象学报, 78(3): 317−331. doi: 10.11676/qxxb2020.022 Wang H J, Ren H L, Chen H P, et al. 2020. Highlights of climate prediction study and operation in China over the past decades [J]. Acta Meteorologica Sinica (in Chinese), 78(3): 317−331. doi: 10.11676/qxxb2020.022
[58]	Wang B, Biasutti M, Byrne M P, et al. 2021. Monsoons climate change assessment [J]. Bull. Amer. Meteor. Soc., 102(1): .E1−E19. doi: 10.1175/BAMS-D-19-0335.1
[59]	Webster P J. 1981. Monsoons [J]. Scientific American, 245(2): 108−118. doi: 10.1038/scientificamerican0881-108
[60]	吴国雄, 丑纪范, 刘屹岷, 等. 2003. 副热带高压研究进展及展望 [J]. 大气科学, 27(4): 503−517. doi: 10.3878/j.issn.1006-9895.2003.04.06 Wu G X, Chou J F, Liu Y M, et al. 2003. Review and prospect of the study on the subtropical Anticyclone [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 27(4): 503−517. doi: 10.3878/j.issn.1006-9895.2003.04.06
[61]	Wu R G, Hu Z Z, Kirtman B P. 2003. Evolution of ENSO-related rainfall anomalies in East Asia [J]. J. Climate, 16(22): 3742−3758. doi:10.1175/1520-0442(2003)016<3742:EOERAI>2.0.CO;2
[62]	Xu Y, Gao X J, Giorgi F., et al 2018. Projected changes in temperature and precipitation extremes over China as measured by 50-yr return values and periods based on a CMIP5 ensemble [J]. Adv. Atmos. Sci., 35(4): 376−388. doi: 10.1007/s00376-017-6269-1
[63]	Yang Q, Ma Z G, Xu B L. 2017a. Modulation of monthly precipitation patterns over East China by the Pacific Decadal Oscillation [J]. Climatic Change, 144(3): 405−417. doi: 10.1007/s10584-016-1662-9
[64]	Yang Q, Ma Z G, Fan X G, et al. 2017b. Decadal modulation of precipitation patterns over eastern China by sea surface temperature anomalies [J]. J. Climate, 30(17): 7017−7033. doi: 10.1175/jcli-d-16-0793.1
[65]	杨阳, 戴新刚, 唐恒伟, 等. 2019. CMIP5模式降水订正法及未来30年中国降水预估 [J]. 气候与环境研究, 24(6): 769−784. doi: 10.3878/j.issn.1006-9585.2019.19021 Yang Y, Dai X G, Tang H W, et al. 2019. CMIP5 model precipitation bias-correction methods and projected China precipitation for the next 30 years [J]. Climatic and Environmental Research (in Chinese), 24(6): 769−784. doi: 10.3878/j.issn.1006-9585.2019.19021
[66]	Yun K S, Ha K J, Yeh S W, et al. 2015. Critical role of boreal summer North Pacific subtropical highs in ENSO transition [J]. Clim. Dyn. , 44(7–8): 1979–1992. doi: 10.1007/s00382-014-2193-6
[67]	张蓓, 戴新刚. 2016. 2006~2013年CMIP5模式中国降水预估误差分析 [J]. 大气科学, 40(5): 981−994. doi: 10.3878/j.issn.1006-9895.1511.15212 Zhang B, Dai X G. 2016. Assessment of the deviation of China precipitation projected by CMIP5 models for 2006–2013 [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 40(5): 981−994. doi: 10.3878/j.issn.1006-9895.1511.15212
[68]	张凯静, 汪萍, 戴新刚, 等. 2017. 中国降水回归模型设计策略与回报检验 [J]. 海洋气象学报, 37(3): 27−35. doi: 10.19513/j.cnki.issn2096-3599.2017.03.004 Zhang K J, Wang P, Dai X G, et al. 2017. The design strategy and hindcasting experiment of China precipitation using regression model [J]. J. Marine Meteor (in Chinese), 37(3): 27−35. doi: 10.19513/j.cnki.issn2096-3599.2017.03.004
[69]	张宏杰, 武亮, 黄荣辉. 2018. 两类El Niño型对西北太平洋季风槽及热带气旋生成的可能影响 [J]. 气候与环境研究, 23(2): 150−160. doi: 10.3878/j.issn.1006-9585.2017.17055 Zhang H J, Wu L, Huang R H. 2018. Possible impacts of two types of El Niño events on the western north Pacific monsoon trough and tropical cyclogenesis [J]. Climatic and Environmental Research (in Chinese), 23(2): 150−160. doi: 10.3878/j.issn.1006-9585.2017.17055
[70]	张蓓, 戴新刚, 杨阳. 2019. 21世纪前期中国降水预估及其订正 [J]. 大气科学, 43(6): 1385−1398. doi: 10.3878/j.issn.1006-9895.1902.18221 Zhang B, Dai X G, Yang Y. 2019. Projection of China precipitation and its bias correction for the early 21st century [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 43(6): 1385−1398. doi: 10.3878/j.issn.1006-9895.1902.18221
[71]	Zhong Y H, Yang M Z, Yuan C X. 2020. Temporal and spatial characteristics of summer extreme precipitation in Eastern China and possible causalities [J]. Scientific Research, 8(6): 36−46. doi: 10.4236/gep.2020.86004
[72]	朱志伟, 何金海. 2013. 东亚副热带季风的季节转变特征及其可能机理 [J]. 热带气象学报, 29(2): 245−254. doi: 10.3969/j.issn.1004-4965.2013.02.008 Zhu Z W, He J H. 2013. Seasonal transition of East Asian subtropical monsoon and its possible mechanism [J]. J. Trop. Meteor. (in Chinese), 29(2): 245−254. doi: 10.3969/j.issn.1004-4965.2013.02.008
[73]	Zhu Z W, Li T, He J H. 2014. Out-of-phase relationship between boreal spring and summer decadal rainfall changes in southern China [J]. J. Climate, 27(3): 1083−1099. doi: 10.1175/JCLI-D-13-00180.1