Using the rainfall prediction data of the Global Ensemble Forecast System from April 1 to September 30, 2017 and the rainfall observation data from the meteorological stations in the Yalong River Basin, the rainfall prediction data was calibrated by the ensemble model output statistics based on the generalized extreme value distribution, and the calibration results obtained from two models were compared and analyzed. The results show that the ensemble member mean calibration model can effectively address the problem of rainfall overestimation, which is always the case with the original prediction model. Furthermore, its prediction result is significantly better than that of the ensemble member calibration model, which is limited by the over-fitting problem due to the increase of model parameters, thus the latter is not applicable to the rainfall prediction of the Yalong River Basin. However, the accuracy of the prediction results of the ensemble member mean calibration model varies significantly in different basins and tends to underestimate the large rainfalls in the basin. Therefore, further research on this method should be conducted targeting at improving the prediction accuracy of extreme rainfalls.