A new precipitation correction forecast method for ECMWF is proposed by using Convolution Neural Network (CNN) and Random Forest Regression (RFR) models. In the new method, the grades of rainfall forecast values of stations are divided according to the ECMWF model, and then the high correlation factor matrix corresponding to different grades is calculated. The CNN model is used to train the comprehensive features of the high correlation matrix. Finally, the features highly correlated with the forecast stations and the factors by the ECMWF precipitation field interpolating to the forecast stations are served as the RFR forecast modeling inputs. The graded and nongraded 24 hour precipitation correction forecast experiments for 10 stations are conducted. The results show that the MAE and RMSE errors of the ECMWF model precipitation grading correction forecast method proposed in this paper are reduced by 20% and 15%, respectively, when compared with the forecast method of ECMWF rainfall forecast field interpolating to stations. Meanwhile, the TS (Threat Score) of 24 hour rainstorms at 10 stations is 0.32, which is significantly higher than that of the EC interpolation (0.19). In addition, compared with the traditional numerical prediction model product correction method using the same forecast model for the full sample (nongraded), the grading correction forecast method proposed in this paper shows a higher forecast sill in the overall forecast accuracy and the heavy rainfall forecast TS score.