Assessing the capability of sub-seasonal dynamic model forecast of precipitation and proposing probable correction method is quite an important topic in current climate research eld. From the perspective of rainfall amount, rainy days and rainfall-belt evolution, the sub-seasonal forecast ability of the European Centre for Medium-Range Weather Forecasts (ECMWF) model for the main rainy-season precipitation in eastern China is evaluated up to 4-week (25-31 days) lead time. The evaluation results show that the forecast biases increase gradually with the increase of forecast lead time, characterized by the predicted rainfall amount obviously higher than observation and the rainy days much longer than observation. In order to decrease the forecast biases of rainfall amount and rainy days, the rainy day based correction (RDC) method is proposed in this study. Cross validation results of the RDC method indicate that the spatial correlation coe cient (SCC) of rainfall amount forecast of the ECMWF model with the observation increases by 0.61%-1.56% and the root mean square error (RMSE) decreases by 3.5%-7.6% up to 4-week lead time. While RDC method also modi es the number of rainy days, the SCC of rainy days increases by 12.96%-18.62%, and the RMSE decreases by 56.49%-63.78%. Furthermore, the problem of the number of continuous rainy days being too long in the model forecast is also improved by using the RDC method. Therefore, the RDC method presents the pretty good performance to improve the sub-seasonal forecast on rainfall amount, total rainy days and maximum continuous rainy days, which may be further applied in other models' sub-seasonal forecasts.