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Various atmospheric systems and the indexes affecting precipitation and temperature in China are examined. The parameters of high independence and high correlation coefficients were chosen as prediction factors to predict the monthly precipitation and temperature of 160 observation stations through downscaling method. By using the filtration method to determine primary prediction factors and establishing the linear regression equations dynamically, the Regional Monthly Climate Forecast Model (RMCFM) is built up, in which the operational interface for communication between people and computer is set up. RMCFM can make monthly climate prediction of monthly precipitation and temperature over 160 stations in China. Practice proved that RMCFM is of high calculating speed and clear structure, and easy to operate. The real time daily grid reanalysis data in NCEP/NCAR were used as pretreatment data for RMCFM. It is proved that RMCFM improved the capability of regional climate prediction.