The main objective of this study is to develop a predictor variable selection method based on rough set theory (RST) for runoff prediction, according to the different influence of different climate variables in different grid point on the runoff. The selected predictor variables were used as downscaling analysis predictors. Multiple linear regression (MLR), back propagation neural network (BPNN) and Bayesian least square support vector machine (Bay-LSSVM) statistical downscaling models were used to predict the monthly runoff of Danjiang Kou reservoir. NCEP/NCAR reanalysis data was utilized to establish the statistical relationship between the larger scale climatic predictors and observed runoff. Comparing with the performance of the statistical downscaling models without predictor variable selection, the models based on predictor variable selection were improved. Predictor variable selection based on RST not only reduced the dimension and noise of the predictor variables dataset greatly in each grid, but also enhanced the performance of statistical downscaling models.