Using the historical precipitation observation data in the Haihe River Basin and the ECMWF ensemble prediction, the 289 grid points in the Haihe River Basin are modeled with Bayesian Processor of Output (BPO), which revise the determine precipitation forecast of the ensemble members to the Bayesian precipitation probability distribution and probability density, and obtain an effective informativeness score (IS) representing the forecasting ability of the ensemble members. Based on the IS values of 51 members, the Bayesian probability forecast information of each member is fused to obtain an integrated Bayesian precipitation probability forecast representing the uncertainty of the ECMWF ensemble forecast. The RPS and BS tests are used. The results show that the reliability of the integrated Bayesian precipitation probability forecast in the Haihe River Basin is higher than the direct probability forecast obtained by the integrated forecast.