To investigate how uncertainty in precipitation forecasts impacts flood forecasting,the THORPEX Interactive Grand Global Ensemble (TIGGE) data extracted from the China Meteorological Administration (CMA),the National Center for Environmental Prediction (NCEP) and the European Center for Medium-range Weather Forecast (ECMWF) were used to establish the GR4J hydrological model such that probabilistic ensemble flood forecasting is explored for the Three Gorges Reservoir. The effectiveness of four statistical post-processing methods,including Bayesian Model Averaging (BMA),Copula-BMA,Ensemble Model Output Statistics (EMOS) and the Modified Bayesian Model Averaging (M-BMA) methods,were compared and analyzed. The results showed that each of the four methods could provide a reasonable and reliable confidence interval on prediction. Besides,compared with the raw deterministic forecasts,the forecast accuracy of expected values associated with the four methods was improved,where the forecast error in water volume was significantly reduced. Furthermore,the M-BMA method performed the best because it considered the heteroscedasticity of the predictive distribution,without conducting a normal transformation,which could be much simpler and more flexible in practice.