Abstract: This study uses an Artificial Neural Network (ANN) technique to establish a non-linear relationship between the large scale atmospheric circulation and local surface rainfall. The method involves the use of statistical downscaling applied to outputs from Eta model. In this sense, prognostic equations were developed for 18 locations using the ANN. This method uses as predictors numerical weather products from the Eta model and surface rainfall as predictand. The objective is to generate site-specific quantitative forecasts of daily rainfall. The selection of ANN input variables are based on the prevailing synoptic weather conditions over southeastern Brazil. Several statistics are calculated to examine the performance of the models. It is found that during summer periods the skill score indicates an ANN improvement over Eta model by 40 %. In the winter period ANN improves RMSE in 80% respect to Eta model. Overall, ANN is efficient in predicting continuous rainfall periods associated to cold fronts and SCAZ during the summer and rainfall events associated with cold front and CVUL originating from middle latitude in winter. Also during winter, the ANN is more efficient, because the synoptic systems are better defined by the variables derived from Eta model.