A Statistical Prediction Model for Summer Precipitation in China Based on TSD Method and EOF Modes’ Time Coefficients

Abstract

It is a challenge to improve the skill of seasonal precipitation prediction, because there are many factors affecting summer precipitation in China, which are found on different time scales and have complex interactions with each other. For these reasons, we establish a prediction model with the time-scale decomposition (TSD) method to investigate whether the TSD has an improving effect on the prediction skill of summer precipitation in China. Using this statistical model, the predictors and predictands will be separated into interannual and interdecadal time scales, after which Empirical Orthogonal Function (EOF) decomposition is performed on these two components, and their time coefficients are predicted, respectively. The hindcast cross-validation results show that the model without TSD has prediction skills only in some regions of East China and South China. Compared with the model without TSD, surprisingly, the model with TSD can significantly improve the prediction performance in more regions in China, such as Xinjiang Province and Northeast China. The anomaly correlation coefficients (ACC) between hindcast precipitation with TSD and observation are higher in most years than that without TSD. The results of the independent sample test show that the forecast model with TSD has a stable and gratifying prediction skill, and the averaged ACC is increased by more than 0.1.