Abstract: The climate system is a high order nonlinear system with dissipation. In recent years, the BP neural network algorithm and the Support Vector Machine (SVM) algorithm are applied widely in the short range climate forecast for its superiority in handling nonlinear time series problem. Besides, the climatic time series are non stationary, so the signal needs processing to improve its predication result. The Empirical Mode Decomposition (EMD) algorithm introduced by Huang is used to stabilize the climatic time series. Combined with the SVM algorithm, it's used for short range climate forecast and applied to the seasonal precipitation forecast in Guangxi. The EMD algorithm decomposes non stationary signal into several Intrinsic Mode Functions (IMF) components and a remainder with stationary. EMD algorithm doesn't provide a good solution for the endpoints extremes problem, and the extreme extending method is adopted as the endpoints continuation method for short range climate forecast. Anomaly percentage of accumulated precipitation data are analyzed, which are observed at 88 meteorological observatories in Guangxi from June to August during 1957—2005. Using the EMD algorithm, the time series being standardized are decomposed into four IMF components and a remainder; then a SVM model is built for each component, and the forecasts are composed to the final forecast result. For comparison, BP neural network algorithm and SVM algorithm are adopted to forecast respectively without the EMD algorithm. Analysis on the predicted values and errors show that, without being processed with EMD, errors of the SVM algorithm are smaller than that of the BP neural network algorithm. So it proves that the generalization capability of BP is weaker than SVM when processing the small sample size problem, whereas SVM algorithm follows the structural risk minimization, and can coincidence the change trend better in condition of finite samples. It shows that the results of the EMD method combined with the SVM algorithm are more accurate. It illustrates that the EMD algorithm can reflect the regularity in different time scales of time series via decomposing into a collection of components with stationarity, which is more suitable for predicting with machine learning methods. The superiority of this scheme makes it widely applicable in precipitation forecast.