Abstract
Southern China is prone to floods during the wet season, and regional extreme precipitation is expected to become more frequent given the context of climate change. However, the prediction skills and predictability of the spatial distribution of extreme precipitation days (EPDs) over southern China remain unclear. To address this issue, through observational diagnosis and numerical experiment, the present study revealed the formation mechanisms of the two leading modes of pre-summer (May and June) EPDs in southern China, and established a physical-based empirical model (P-E model) to predict the distribution of EPDs in the region. The results revealed the following: (1) the first mode of pre-summer EPDs in southern China displayed a uniform pattern, while the second mode presented a meridional dipole pattern. The uniform pattern of the first mode is associated with the tropical western North Pacific anomalous anticyclone (TWPAC), whereas the meridional dipole pattern of the second mode is related to the subtropical western North Pacific anomalous anticyclone (SWPAC); (2) the spring North Atlantic tripole sea surface temperature (SST) pattern, the northern North America surface air temperature, and the snow cover depth over central Siberia contribute to the variation of the first mode and TWPAC, whereas the spring extratropical North Atlantic dipole SST pattern and the spring change of sea ice concentration over the Barents and Kara seas are physically linked with the variation of the second mode and SWPAC; (3) based on these predictors, the established P-E model demonstrated predictive skill with regard to the principal components of the first two modes, and the spatial distribution of EPDs in southern China was reconstructed. The areal mean temporal correlation coefficient skill for the independent prediction period (2011–2021) was 0.34, while for the pattern correlation coefficient skill averaged over the entire period was 0.28. In comparison with the maximum attainable skills, both metrics indicate potential for improvement. The findings of this study represent a reference for the prediction and predictability of the spatial distribution of pre-summer EPDs in southern China.
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Data availability
CN05.1 data were obtained from the China Meteorology Administration’s National Climate Center. The monthly geopotential height and wind fields at multiple levels, surface air temperature at 2 m, and snow depth from the fifth major global reanalysis produced by ERA5 are openly available at https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5. The monthly mean SST and SIC data extracted from the Hadley Centre Sea Ice and Sea Surface Temperature dataset are available at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The monthly mean sea surface temperature data from the improved Extended Reconstructed SST dataset (version 5) are available at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The global monthly mean precipitation data obtained from the Global Precipitation Climatology Project datasets can be downloaded from https://psl.noaa.gov/data/gridded/data.gpcp.html.
References
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P-P, Janowiak J et al (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). J Hydrometeorol 4(6):1147–1167. https://doi.org/10.1175/1525-7541(2003)004%3c1147:TVGPCP%3e2.0.CO;2
Bodri L, Čermák V (2000) Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Adv Eng Softw 31(5):311–321. https://doi.org/10.1016/S0965-9978(99)00063-0
Chen W, Lee J-Y, Lu R, Dong B, Ha K-J (2014) Intensified impact of tropical Atlantic SST on the western North Pacific summer climate under a weakened Atlantic thermohaline circulation. Clim Dyn 45(7–8):2033–2046. https://doi.org/10.1007/s00382-014-2454-4
Easterling DR, Meehl GA, Parmesan C, Changnon SA, Karl TR, Mearns LO (2000) Climate extremes: observations, modeling, and impacts. Science 289(5487):2068–2074. https://doi.org/10.1126/science.289.5487.2068
Enomoto T, Hoskins BJ, Matsuda Y (2003) The formation mechanism of the Bonin high in August. Q J Roy Meteorol Soc 129(587):157–178. https://doi.org/10.1256/qj.01.211
Frich P, Alexander LV, Della-Marta P, Gleason B, Haylock M, Tank AK, Peterson T (2002) Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim Res 19(3):193–212. https://doi.org/10.3354/cr019193
Fu S, Zhu Z, Lu R (2022) Changes in the factors controlling northeast Asian spring surface air temperature in the past 60 years. Clim Dyn. https://doi.org/10.1007/s00382-022-06569-7
Gao T, Wang HJ, Zhou T (2017) Changes of extreme precipitation and nonlinear influence of climate variables over monsoon region in China. Atmos Res 197:379–389. https://doi.org/10.1016/j.atmosres.2017.07.017
Gao M, Wang B, Yang J, Dong W (2018) Are peak summer sultry heat wave days over Yangtze-Huaihe River basin predictable? J Clim. https://doi.org/10.1175/JCLI-D-17-0342.1
Gao C, Li G, Xu B, Li X (2020) Effect of spring soil moisture over the Indo-China Peninsula on the following summer extreme precipitation events over the Yangtze River basin. Clim Dyn 54(9–10):3845–3861. https://doi.org/10.1007/s00382-020-05187-5
Gill AE (1980) Some simple solutions for heat-induced tropical circulation. Q J Roy Meteorol Soc 106(449):447–462. https://doi.org/10.1002/qj.49710644905
Ham Y-G, Kug J-S, Park J-Y, Jin F-F (2013) Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events. Nat Geosci 6(2):112–116. https://doi.org/10.1038/ngeo1686
Held IM, Suarez MJ (1994) A proposal for the intercomparison of the dynamical cores of atmospheric general circulation models. B Am Meteorol Soc 75(10):1825–1830. https://doi.org/10.1175/1520-0477(1994)075%3c1825:Apftio%3e2.0.Co;2
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J et al (2020) The ERA5 global reanalysis. Q J Roy Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/qj.3803
Hong C, Chang T, Hsu H (2014) Enhanced relationship between the tropical Atlantic SST and the summertime western North Pacific subtropical high after the early 1980s. J Geophys Res-Atmos 119(7):3715–3722. https://doi.org/10.1002/2013jd021394
Hsu P-C, Xie J, Lee J, Zhu Z, Li Y, Chen B, Zhang S (2023) Multiscale interactions between seasonal-mean state, intraseasonal oscillation and synoptic disturbances driving the devastating floods in China’s Henan Province in July 2021. Weather Clim Extremes 39:100541. https://doi.org/10.1016/j.wace.2022.100541
Hu Y, Deng Y, Zhou Z, Cui C, Dong X (2018) A statistical and dynamical characterization of large-scale circulation patterns associated with summer extreme precipitation over the middle reaches of Yangtze river. Clim Dyn 52(9–10):6213–6228. https://doi.org/10.1007/s00382-018-4501-z
Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Zhang H-M et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J Clim 30(20):8179–8205. https://doi.org/10.1175/JCLI-D-16-0836.1
Jiang L, Li T (2021) Impacts of tropical north Atlantic and equatorial Atlantic SST anomalies on ENSO. J Clim 34(14):5635–5655. https://doi.org/10.1175/jcli-d-20-0835.1
Jiang Y, Liu X, Yang X-Q, Wang M (2013) A numerical study of the effect of different aerosol types on East Asian summer clouds and precipitation. Atmos Environ 70:51–63. https://doi.org/10.1016/j.atmosenv.2012.12.039
Lee JY, Lee SS, Wang B, Ha KJ, Jhun JG (2013) Seasonal prediction and predictability of the Asian winter temperature variability. Clim Dyn 41(3–4):573–587. https://doi.org/10.1007/s00382-012-1588-5
Li J, Wang B (2015) How predictable is the anomaly pattern of the Indian summer rainfall? Clim Dyn 46(9–10):2847–2861. https://doi.org/10.1007/s00382-015-2735-6
Li J, Wang B (2018) Predictability of summer extreme precipitation days over eastern China. Clim Dyn 51(11–12):4543–4554. https://doi.org/10.1007/s00382-017-3848-x
Li J, Zheng F, Sun C, Feng J, Wang J (2019) Pathways of influence of the northern hemisphere mid-high latitudes on East Asian climate: a review. Adv Atmos Sci 36(9):902–921. https://doi.org/10.1007/s00376-019-8236-5
Li C, Zwiers F, Zhang X, Li G, Sun Y, Wehner M (2021a) Changes in annual extremes of daily temperature and precipitation in CMIP6 models. J Clim 34(9):3441–3460. https://doi.org/10.1175/jcli-d-19-1013.1
Li J, Huo R, Chen H, Zhao Y, Zhao T (2021b) Comparative assessment and future prediction using CMIP6 and CMIP5 for annual precipitation and extreme precipitation simulation. Front Earth Sci. https://doi.org/10.3389/feart.2021.687976
Long Y, Li J, Zhu Z, Zhang J (2022) Predictability of the anomaly pattern of summer extreme high-temperature days over southern China. Clim Dyn 59(3–4):1027–1041. https://doi.org/10.1007/s00382-022-06170-y
Lu R, Zhu Z, Li T, Zhang H (2020) Interannual and interdecadal variabilities of spring rainfall over Northeast China and their associated sea surface temperature anomaly forcings. J Clim 33(4):1423–1435. https://doi.org/10.1175/jcli-d-19-0302.1
Luo X, Wang B (2017) How predictable is the winter extremely cold days over temperate East Asia? Clim Dyn. https://doi.org/10.1007/s00382-016-3222-4
Murphy AH (1988) Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon Weather Rev 116:2417–2424. https://doi.org/10.1175/1520-0493(1988)116%3c2417:SSBOTM%3e2.0.CO;2
Ning L, Liu J, Wang B (2017) How does the South Asian high influence extreme precipitation over eastern China? J Geophys Res-Atmos 122(8):4281–4298. https://doi.org/10.1002/2016jd026075
North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling errors in the estimation of empirical orthogonal functions. Mon Weather Rev 110(7):699–706. https://doi.org/10.1175/1520-0493(1982)110%3c0699:SEITEO%3e2.0.CO;2
Ou T, Chen D, Linderholm HW, Jeong J-H (2013) Evaluation of global climate models in simulating extreme precipitation in China. Tellus A 65(1):19799. https://doi.org/10.3402/tellusa.v65i0.19799
Pan X, Li T, Sun Y, Zhu Z (2021) Cause of extreme heavy and persistent rainfall over Yangtze River in summer 2020. Adv Atmos Sci 38(12):1980–1993. https://doi.org/10.1007/s00376-021-0433-3
Park J-H, Kug J-S, Yang Y-M, Oh H, Zhao J, Wu Y (2022) Role of climatological North Pacific high in the North Tropical Atlantic-ENSO connection. J Clim. https://doi.org/10.1175/JCLI-D-21-0933.1
Peng Y, Zhao X, Wu D, Tang B, Xu P, Du X, Wang H (2018) Spatiotemporal variability in extreme precipitation in China from observations and projections. Water. https://doi.org/10.3390/w10081089
Qiao S, Feng G (2016) Impact of the December North Atlantic Oscillation on the following February East Asian trough. J Geophys Res-Atmos 121(17):10074–10088. https://doi.org/10.1002/2016JD025007
Qin P, Xie Z, Zou J, Liu S, Chen S (2021) Future precipitation extremes in China under climate change and their physical quantification based on a regional climate model and CMIP5 model simulations. Adv Atmos Sci 38(3):460–479. https://doi.org/10.1007/s00376-020-0141-4
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, Kent EC, Kaplan A (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J Geophys Res. https://doi.org/10.1029/2002JD002670
Rong X, Zhang R, Li T (2010) Impacts of Atlantic sea surface temperature anomalies on Indo-East Asian summer monsoon-ENSO relationship. Chin Sci B-Chin 55(22):2458–2468. https://doi.org/10.1007/s11434-010-3098-3
Shang W, Li S, Ren X, Duan K (2020) Event-based extreme precipitation in Central-Eastern China: large-scale anomalies and teleconnections. Clim Dyn 54(3–4):2347–2360. https://doi.org/10.1007/s00382-019-05116-1
Tian Y, Gao Y, Guo D (2021) The Relationship between melt season sea ice over the Bering sea and summer precipitation over Mid-Latitude East Asia. Adv Atmos Sci 38(6):918–930. https://doi.org/10.1007/s00376-021-0348-z
Wang ZY, Plate EJ (2002) Recent flood disasters in China. Proc Inst Civ Eng-Water Manag 154(3):177–188. https://doi.org/10.1680/wame.2002.154.3.177
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(5):1118–1128. https://doi.org/10.1007/s00376-010-0128-7
Wang B, Wu R, Fu X (2000) Pacific-East Asian teleconnection: how does ENSO affect East Asian climate? J Clim 13(9):1517–1536. https://doi.org/10.1175/1520-0442(2000)013%3c1517:PEATHD%3e2.0.CO;2
Wang B, Wu R, Li T (2003) Atmosphere–warm ocean interaction and its impact on Asian–Australian monsoon variability. https://doi.org/10.1175/1520-0442(2003)16<1195:AOIAII>2.0.CO;2
Wang B, Lee JY, Kang IS, Shukla J (2007) Coupled predictability of seasonal tropical precipitation. Clivar Exch 12:17–18
Wang B, Liu J, Yang J, Zhou T, Wu Z (2009) Distinct principal modes of Early and Late summer rainfall anomalies in East Asia. J Clim 22(13):3864–3875. https://doi.org/10.1175/2009jcli2850.1
Wang B, Lee J-Y, Xiang B (2014) Asian summer monsoon rainfall predictability: a predictable mode analysis. Clim Dyn 44(1–2):61–74. https://doi.org/10.1007/s00382-014-2218-1
Wang B, Xiang B, Li J, Webster PJ, Rajeevan MN, Liu J, Ha KJ (2015) Rethinking Indian monsoon rainfall prediction in the context of recent global warming. Nat Commun 6:7154. https://doi.org/10.1038/ncomms8154
Wei W, Yan Z, Jones PD (2019) A decision-tree approach to seasonal prediction of extreme precipitation in eastern China. Int J Climatol 40(1):255–272. https://doi.org/10.1002/joc.6207
Wei K, Ouyang C, Duan H, Li Y, Chen M, Ma J et al (2020) Reflections on the catastrophic 2020 Yangtze River Basin flooding in Southern China. Innovations 1(2):100038. https://doi.org/10.1016/j.xinn.2020.100038
Wilhelmi OV, Morss RE (2013) Integrated analysis of societal vulnerability in an extreme precipitation event: a Fort Collins case study. Environ Sci Policy 26:49–62. https://doi.org/10.1016/j.envsci.2012.07.005
Wu J, Gao XJ (2013) A gridded daily observation dataset over China region and comparison with the other datasets (in Chinese). Chin J Geophys 56(4):1102–1111
Wu R, Wen Z, Yang S, Li Y (2010) An interdecadal change in Southern China summer rainfall around 1992/93. J Clim 23(9):2389–2403. https://doi.org/10.1175/2009jcli3336.1
Wu Z, Li J, Jiang Z, He J, Zhu X (2012) Possible effects of the North Atlantic oscillation on the strengthening relationship between the East Asian summer monsoon and ENSO. Int J Climatol 32(5):794–800. https://doi.org/10.1002/joc.2309
Wu J, Li J, Zhu Z, Hsu P-C (2023) Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China. Clim Dyn. https://doi.org/10.1007/s00382-022-06326-w
Xing W, Wang B, Yim SY, Ha KJ (2017) Predictable patterns of the May–June rainfall anomaly over East Asia. J Geophys Res-Atmos 122(4):2203–2217. https://doi.org/10.1002/2016jd025856
Yim S, Wang B, Xing W (2014) Prediction of early summer rainfall over South China by a physical-empirical model. Clim Dyn 43(7–8):1883–1891. https://doi.org/10.1007/s00382-013-2014-3
Yu J, Li T, Tan Z, Zhu Z (2016) Effects of tropical North Atlantic SST on tropical cyclone genesis in the western North Pacific. Clim Dyn 46(3):865–877. https://doi.org/10.1007/s00382
Zeng J, Hsieh W, Shabbar A, Burrows W (2010) Seasonal prediction of winter extreme precipitation over Canada by support vector regression. Hydrol Earth Syst Sci. https://doi.org/10.5194/hess-15-65-2011
Zhai P, Zhang X, Wan H, Pan X (2005) Trends in total precipitation and frequency of daily precipitation extremes over China. J Clim 18(7):1096–1108. https://doi.org/10.1175/JCLI-3318.1
Zhang DL, Lin Y, Zhao P, Yu X, Wang S, Kang H, Ding Y (2013) The Beijing extreme rainfall of 21 July 2012:“Right results” but for wrong reasons. Geophys Res Lett 40(7):1426–1431. https://doi.org/10.1002/grl.50304
Zhang W, Jin FF, Stuecker MF, Wittenberg AT, Timmermann A, Ren HL et al (2016) Unraveling El Niño’s impact on the East Asian monsoon and Yangtze River summer flooding. Geophys Res Lett. https://doi.org/10.1002/2016gl071190
Zhang Q, Zheng Y, Singh VP, Luo M, Xie Z (2017a) Summer extreme precipitation in eastern China: mechanisms and impacts. J Geophys Res-Atmos 122(5):2766–2778. https://doi.org/10.1002/2016JD025913
Zhang R, Zhang R, Zuo Z (2017b) Impact of Eurasian spring snow decrement on East Asian summer precipitation. J Clim 30(9):3421–3437. https://doi.org/10.1175/jcli-d-16-0214.1
Zhang K, Li J, Zhu Z, Li T (2021) Implications from subseasonal prediction skills of the prolonged heavy snow event over southern China in early 2008. Adv Atmos Sci 38(11):1873–1888. https://doi.org/10.1007/s00376-021-0402-x
Zhang P, Wu Z, Zhu Z, Jin R (2022) Promoting seasonal prediction capability of the early autumn tropical cyclone formation frequency over the western North Pacific: effect of Arctic sea ice. Environ Res Lett 17(12):124012. https://doi.org/10.1088/1748-9326/aca2c0
Zhao J, Han Z, Zuo J, Yang L, Yang J, Xiong K et al (2022) Oceanic drivers and empirical prediction of interannual rainfall variability in late summer over Northeast China. Clim Dyn 58:1–18. https://doi.org/10.1007/s00382-021-05945-z
Zheng J, Wang C (2021) Influences of three oceans on record-breaking rainfall over the Yangtze River Valley in June 2020. Sci China Earth Sci 64(10):1607–1618. https://doi.org/10.1007/s11430-020-9758-9
Zhou X, Bai Z, Yang Y (2017) Linking trends in urban extreme rainfall to urban flooding in China. Int J Climatol 37(13):4586–4593. https://doi.org/10.1126/science.289.5487.2068
Zhou Z, Li J, Chen H, Zhu Z (2023) Seasonal prediction of extreme high temperature days in southwestern China based on physical precursors. Adv Atmos Sci. https://doi.org/10.1007/s00376-022-2075-5
Zhu Z, Li T (2016) A new paradigm for continental U.S. summer rainfall variability: Asia-North America teleconnection. J Clim 29:7313–7327. https://doi.org/10.1175/JCLI-D-16-0137.1
Zhu Z, Li T (2017) Statistical extended-range forecast of winter surface air temperature and extremely cold days over China. QJR Meteorol Soc 704(143):1528–1538. https://doi.org/10.1002/qj.3023
Zhu Z, Li T (2018a) Extended-range forecasting of Chinese summer surface air temperature and heat waves. Clim Dyn 50(5–6):2007–2021. https://doi.org/10.1007/s00382-017-3733-7
Zhu Z, Li T (2018b) Amplified contiguous United States summer rainfall variability induced by East Asian monsoon interdecadal change. Clim Dyn 50(9):3523–3536. https://doi.org/10.1007/s00382-017-3821-8
Zhu Z, Lu R, Yan H, Li W, Li T, He J (2020) The dynamic origin of the interannual variability of West China autumn rainfall. J Clim 33(22):9643–9652. https://doi.org/10.1175/JCLI-D-20-0097.1
Zhu Z, Lu R, Fu S, Chen H (2022) Alternation of the atmospheric teleconnections associated with the Northeast China spring rainfall during a recent 60-year period. Adv Atmos Sci 40(1):168–176. https://doi.org/10.1007/s00376-022-2024-3
Zuo J, Li W, Sun C, Xu L, Ren H-L (2013) Impact of the North Atlantic sea surface temperature tripole on the East Asian summer monsoon. Adv Atmos Sci 30(4):1173–1186. https://doi.org/10.1007/s00376-012-2125-5
Acknowledgements
This work was supported by the National Key R&D Program of China (2022YFF0801702), the National Natural Science Foundation of China (Grants: 42088101 and 42175033) and the High-Performance Computing Center of Nanjing University of Information Science and Technology.
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This work was supported by the National Key R&D Program of China (2022YFF0801702) and the National Natural Science Foundation of China (Grants: 42088101 and 42175033).
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JL and ZZ contributed to conception and design. Material preparation, data collection, and observational analysis were performed by CZ and ZZ. The numerical experiments were performed by YY and RL. The first draft of the manuscript was written by JL and ZZ. All authors read and approved the final manuscript.
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Li, J., Zheng, C., Yang, Y. et al. Predictability of spatial distribution of pre-summer extreme precipitation days over southern China revealed by the physical-based empirical model. Clim Dyn 61, 2299–2316 (2023). https://doi.org/10.1007/s00382-023-06681-2
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DOI: https://doi.org/10.1007/s00382-023-06681-2