A preliminary test of the summer rainfall prediction in China based on the land surface thermal factors

Based on the correlation analysis between the April soil temperature (ST) over the Eurasian continent, the global sea surface temperature (SST) in the previous winter and the summer precipitation in eastern China, STs and SSTs over the key regions are selected as the predictors of the summer rainfall prediction. Based on the observations during the period 1961-1990, the Barnett-Preisendorfer canonical correlation analysis (BP-CCA) and the ensemble canonical correlation analysis (ECC) are used to build statistical prediction models of the summer rainfall in eastern China with taking ST, SST or SST combined with ST as the predictors, respectively. The independent sample test is performed by using the observations during 1991-2010. The results show that the model using SST combined with ST as predictor exhibits higher predictive skill than that only using one of them, implying that consideration of the ST improves the performance of the prediction model. The prediction model based on the land surface thermal factors has some skills in predicting the summer precipitation in eastern China, and the way using the combination of SST with ST shows a better performance in the summer precipitation prediction.