Abstract: A downscaling method taking into account of precipitation regionalization is developed and used in the regional summer precipitation prediction (RSPP) in China. The downscaling is realized by utilizing the optimal subset regression based on the hindcast data of the Coupled Ocean Atmosphere General Climate Model of National Climate Center (CGCM/NCC), the historical reanalysis data, and the observations. The data are detrended in order to remove the influence of the interannual variations on the selection of predictors for the RSPP. Optimal predictors are selected through calculation of anomaly correlation coefficients (ACC) twice to ensure that the highskill areas of the CGCM/NCC are also those of observations, with the ACC value reaching the 0.05 significant level. One-year out cross-validation and independent sample tests indicate that the downscaling method is applicable in the prediction of summer precipitation anomaly across most of China with high and stable accuracy, and is much better than the direct CGCM/NCC prediction. The predictors used in the downscaling method for the RSPP are independent and have strong physical meanings, thus leading to the improvements in the prediction of regional precipitation anomalies.