The support vector machine (SVM), a new general machinery study method based on the frame of statistical study theory, may solve the problems of non-linear classification and regression in sample space and be a effective method of processing the non-liner classification and regression. The complexity and non-linearity of factors of climate change decide the non-linear relationship between the forecast factors and the forecast object, and the SVM provide an effective and feasible way to forecast short-term climate. A SVM non-linear classification model of positive/ negative departure in summer precipitation is developed according to the 15 forecast factors, including the sea surface temperature of Nino area, the southern Oscillation index, the subtropical high area index and the polar vortex area index of Asian region, meanwhile, a SVM regression model of summer precipitation in Yangquan is developed as well.