Abstract: Variables fields such as enstrophy,meridional-wind vary,and zonal-wind vary are derived from monthly 500 hPa geopotential height abnormal fields.In this work,we elect original predictors from monthly 500 hPa geopotential height abnormal fields and their variables in June of 1958～2001,and make comprehensive predictors by way of making empirical orthogonal function(EOF) respectively with the original predictors.A downscaling forecast model based on the back propagation(BP) neural network is built by use of the comprehensive predictors to predict the monthly precipitations in June over Guangxi with the monthly dynamic extended rang forecast products.To contrasted,we also build another BP neural network model with the same predictands by using the former comprehensive predictors elected from 500 hPa geopotential height abnormal fields in May to December of 1957～2000 and January to April of 1958～2001.The two models are tested and results show that the precision of superposition of the downscaling model is better than that of the one based on former comprehensive predictors,but the prediction accuracy of the downscaling model depends on the output of monthly dynamic extended rang forecast.