The empirical mode decomposition (EMD) method has the advantage of dealing with the nonlinear and nonstationary data, making them linearized and stationary. So EMD is adopted to analyze the precipitation data based on the multi time scale viewpoint, and the relatively simple semi period signal with different oscillations are decomposed from the complex nonstationary and nonlinear signal. Then the characteristic intrinsic mode functions (IMFs) are chosen to construct the regression ensemble prediction model (REPM), which is based on the mean generation regression (MGR) method, the mean generation correlation (MGC) method, the rhythm fitting error (RFE) method and the fitting error (FE) method. The results show that the average score of the Ps and the same symbol ratio (SSR) are 68-73 and 50%-58%, respectively, among the four kinds of single models during rainy period in Guangdong for the recent 10 years. However, in the REPM, the average Ps and SSR scores have reached 79.8 and 68.8%, respectively, increasing 10 scores and 10% or so compared with one of the four kinds of single models. Meanwhile, if the SST signals in tropical East Pacific in the previous winter are coupled into the REPM, the Ps and SSR scores have improved, but the SSR scores, 3.1% higher than the former. Therefore, both the multi time scale information extracting from the meteorological elements and the ensemble model construction can improve the accuracy of short term climate prediction.