Seasonal Prediction of Pre-Summer Clustered Heavy Precipitation Days in South China  

作　　者：ZHI Yao HAN Ting-ting JIANG Shun-li WANG Hui-jun HAO Xin LI Shang-feng 智垚;韩婷婷;蒋顺莉;王会军;郝鑫;李尚锋

机构地区：[1]State Key Laboratory of Climate System Prediction and Risk Management/Key Laboratory of Meteorological Disaster,Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University for Information Science and Technology,Nanjing 210044 China [2]Nansen-Zhu International Research Centre,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029 China [3]Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai,Guangdong 519080 China [4]Jilin Provincial Key Laboratory of Changbai Mountain Meteorology&Climate Change,Laboratory of Research for Middle-High Latitude Circulation Systems and East Asian Monsoon,Institute of Meteorological Sciences of Jilin Province,Changchun 130062 China

出　　处：《Journal of Tropical Meteorology》2025年第1期107-119,共13页热带气象学报(英文版)

基　　金：Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004);Science and Technology Development Plan in Jilin Province of China(20230203135SF);National Natural Science Foundation of China(41875119);Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。

摘　　要：Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.

关 键 词：seasonal prediction clustered heavy precipitation South China interannual increment 

分 类 号：P46[天文地球—大气科学及气象学]