Abstract: Guangdong Province is located in low latitude areas and affected by both tropical weather systems and high latitude weather systems.A multi-scale spatial-temporal projection (MSTP) method is developed to predict the monthly and seasonal precipitation in Guangdong.Multi-factor and multi-scale forecasting method can be used to seek for the forecast factor by quantity scale separation through scale decomposition conforming to the physical meaning, thus it can reduce climate non-stationary time series and improve the prediction accuracy.The key feature of MSTP mothod is that it considers not only spatially but also temporally varying large-scale field connection between the predictor and predictand.Based on main modes of the empirical orthogonal function (EOF) analysis, periods are gained from the wavelet analysis and decomposed by Lanczos filtering.According to the correlation between the precipitation and Climate Forecast Systems datasets provided by National Centers for Environmental Prediction dynamic model data (CFSv2), significant influencing factors are selected to predict precipitation with MSTP method.Using the least square error correction method, Guangdong monthly and seasonal precipitation predictions are obtained based on inter-annual increment approach.The test of independent samples from 2006 to 2015 shows the correction can improve the performance of prediction, making PS score of operational test change smoothly.After correction PS scores are improved greatly during 6 years of the hindcast period, and the monthly and seasonal rainfall account for 68.8%.For 87.5% of total samples, the forecast average score is over 70.The prediction effect is closely related to period changes of the precipitation main mode.If the inter-annual period is priority to other period, the prediction effect after correction is significantly higher than that before the correction, otherwise it is poor.The root mean square error within 0.5-1 standard deviation rate after correction is higher than that before corrections.Within 0.5 standard deviations, the monthly and seasonal rainfalls, of which the root mean square error of probability is more than 40%, account for 81.3% after correction comparing to 31.3% before correction.Within 1 standard deviation, the monthly and seasonal rainfalls, of which the root mean square error of probability is more than 70%, account for 56.3% comparing to 50% before correction.It suggests that most of rainfall prediction errors are within 1 standard deviation.Therefore, the prediction from MSTP method can offer important reference to operational prediction of short-term climate prediction for monthly and seasonal prediction in Guangdong.