Abstract: Using the maximal correlation minimum redundancy algorithm and random forest regression algorithm, a rainstorm interpretation forecasting method with numerical prediction products is proposed based on the ensemble prediction system of European Center for Medium-Range Weather Forecasts (ECMWF). The precipitation forecast of 51 members in ECMWF ensemble prediction system are interpolated to weather stations, and then, the maximum related minimum redundancy algorithm is used to filter ensemble members. Finally, several member interpolations that have the highest correlation with the predictand and the least redundancy with each other are selected as input factors of the random forest regression algorithm. Furthemore, in order to make modeling samples of the forecast model more pertinence, the modeling samples are classified using the mean rainfall value of ECMWF ensemble prediction products of 51 members. That is, when the mean precipitation using ECMWF ensemble prediction products at a certain station is relatively large and there is a possibility of precipitation above the storm level, only historical samples containing a large amount of precipitation are selected as modeling samples of the forecasting model. Therefore, the forecasting model reduces the influence of the sunny and wet weather samples on the noise of the forecasting model, so that forecasting model focuses on the training of large precipitation samples. When the mean value of the predicted ECMWF ensemble precipitation at a certain weather station is small, all samples of the weather station (including samples of sunny days and heavy precipitation) are modeled so that the training of the forecasting model can reconcile the heavy rain samples and thus as far as possible to avoid the rainstorm of weather station omissions reported. This method is applied to 89 stations in Guangxi, and a 4-year cross-independent sample test forecast for 2012-2015 is carried out. The business test forecast is carried out in 2016. In the 4-year cross-independent sample test results, rainstorm TS and ETS scores of this method are all improved by 0.04-0.09 and 0.04-0.07, respectively, compared with the average value after interpolation using the precipitation forecast of 51 members in ECMWF ensemble prediction products. Results of the business trial in 2016 show that TS and ETS scores of the method for interpretation rainstorms TS and ETS scores are improved by 0.07 and 0.05, respectively, compared with average values of pre-interpolation methods for the precipitation forecast of 51 members in ECMWF ensemble prediction products. It shows that the proposed rainstorm precipitation method of ECMWF ensemble prediction products has advantageous effects on forecasting and practical application forecast.