Extreme precipitation is likely to be one of the most severe meteorological disasters in China; however, studies on the physical factors affecting precipitation extremes and corresponding prediction models are not accurately available. From a new point of view, the sensible heat flux (SHF) and latent heat flux (LHF), which have significant impacts on summer extreme rainfall in Yangtze River basin (YRB), have been quantified and then selections of the impact factors are conducted. Firstly, a regional extreme precipitation index was applied to determine Regions of Significant Correlation (RSC) by analyzing spatial distribution of correlation coefficients between this index and SHF, LHF, and sea surface temperature (SST) on global ocean scale; then the time series of SHF, LHF, and SST in RSCs during 1967–2010 were selected. Furthermore, other factors that significantly affect variations in precipitation extremes over YRB were also selected. The methods of multiple stepwise regression and leave-one-out cross-validation (LOOCV) were utilized to analyze and test influencing factors and statistical prediction model. The correlation coefficient between observed regional extreme index and model simulation result is 0.85, with significant level at 99%. This suggested that the forecast skill was acceptable although many aspects of the prediction model should be improved. 1. Introduction Temporal and spatial variations in extreme precipitation events often result in serious impacts on human society and ecological environment. And higher frequency of these extremes poses vast catastrophic consequences, including floods, landslides, and urban waterlog (e.g., [1, 2]). In recent years numerous disastrous floods have been documented worldwide, for example, the intense flash flooding occurred in Minnesota, Wisconsin, in the United States, in June, 2012 [3], and the extreme rainfall in Beijing, China, in July, 2012 [4]; all those events have caused devastating social impacts. Moreover, in the context of global climate change, previous studies have suggested that many regions over the world would experience more frequent extreme precipitation with the enhancement of anthropogenic greenhouse gas and aerosol emissions (e.g., [5–8]). Therefore, projection of seasonal variations in precipitation extremes on local and regional scale is overwhelmingly important for reducing casualties and property losses as well as water resource management. However, the two major current methods, for dynamical model and statistics, show low operational skills for forecasting local extreme
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