Abstract: The South China Sea summer monsoon (SCSSM) is a tropical system that plays a key role during the flood season of South China. However, the prediction of the SCSSM strength is difficult by no matter dynamic or statistic methods. Statistic methods are used in practice rather than dynamic model, but empirical-statistic models always have good hindcasting results during the period of building model, while the forecasting skills decrease evidently in practice. Physical-statistic methods have relatively stable predictive skill when the persistence of physical processes is taken into account. Therefore, an integrated technique is introduced based on associated physical processes to establish a predictive model for SCSSM. It is well known that the rainfall of SCSSM has multi-scale climate variability, for example, quasi-biennial and quasi-quadrennial time scale, which are mainly related to TBO (Tropospheric Biennial Oscillation) and ENSO (El Niño-Southern Oscillation), respectively. Based on the corresponding climatic factors, a physical-statistic integrated model is built. Combined with the traditional empirical-statistic method, a new prediction model (namely physical and empirical-statistic integrated model) for SCSSM is developed.First, original data are processed by removing the climatic state (1981-2010) and linear trend, and then anomalous data are filtered on the TBO (12-36 months) and ENSO (36-96 months) time scales since the biennial mode of SCSSM has little connection with the ENSO. Second, regressed results based on climatic factors (e.g., sea surface temperature anomalies in Niño3.4 and the tropical western Pacific, precipitation anomalies over the maritime continent and Australian monsoon region) are assembled according to a discrimination function that is correlation coefficient larger than 0.05 significant level between regressed results and the filtered SCSSM precipitation. Moreover, the rest precipitation with SCSSM inter-annual variations removed is predicted by the traditional empirical-statistic method and results are added to those by the physical-statistic integrated model. Using data throughout 1979-2010, the physical and empirical-statistic integrated model is trained and results of 2011-2016 are predicted for test, compared with that of the empirical-statistic integrated model. It shows that the new model has better prediction skill (9.5% improvements in prediction score and 75% in anomaly correlation coefficient) and relatively stable predicting results. More than that, the new model has some predictive ability for SCSSM rainfall distribution.