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Seasonal prediction of extreme high-temperature days over the Yangtze River basin
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Extreme high temperatures occur frequently over the densely populated Yangtze River basin(YRB)in China during summer,significantly impacting the local economic development and ecological system.However,accurate prediction of extreme high-temperature days in this region remains a challenge.Unfortunately,the Climate Forecast System Version 2(CFSv2)exhibits poor performance in this regard.Thus,based on the interannual increment approach,we develop a hybrid seasonal prediction model over the YRB(HMYRB)to improve the prediction of extreme high-temperature days in summer.The HMYRB relies on the following four predictors:the observed preceding April-May snowmelt in north western Europe;the snow depth in March over the central Siberian Plateau;the CFSv2-forecasted concurrent summer sea surface temperatures around the Maritime Continent;and the 200-hPa geopotential height over the Tibetan Plateau.The HMYRB indicates good capabilities in predicting the interannual variability and trend of extreme high-temperature days,with a markable correlation coefficient of 0.58 and a percentage of the same sign(PSS)of 76％during 1983-2015 in the one-year-out cross-validation.Additionally,the HM YRB maintains high PSS skill(86％)and robustness in the independent prediction period(2016-2022).Furthermore,the HMYRB shows a good performance for years with high occurrence of extreme high-temperature days,with a hit ratio of 40％.These predictors used in HMYRB are beneficial in terms of the prediction skill for the average daily maximum temperature in summer over the YRB,albeit with biases existing in the magnitude.Our study provides promising insights into the prediction of 2022-like hot extremes over the YRB in China.
Hot extremesYangtze River basinCFSv2Seasonal predictionOne-year-out cross-validation
Shifeng PAN、Zhicong YIN、Mingkeng DUAN、Tingting HAN、Yi FAN、Yangyang HUANG、Huijun WANG
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Key Laboratory of Meteorological Disaster,Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210044,China
Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai),Zhuhai 519080,China
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