Bayesian Model Averaging (BMA) is applied to monthly precipitation forecasting in the flood season over the Dongjiang basin to correct the bias of Climate Forecast System version2 (CFSv2). In the meantime

Standardized Anomaly (SA) is used to quantify the precipitation abnormality and incorporated into the deterministic and ensemble forecasting. A better precipitation forecasting model is then established by the combination of BMA and SA to improve accuracy of long-term precipitation forecasting in the Dongjiang basin. Conclusions are drawn as follows: ① The 50th percentile and below of ensemble forecasting have poor skill

whereas the 75th percentile is usually in agreement with observations. However

BMA has disadvantage in that it underestimates precipitation amount when extreme events occur. ② The value of SA based on the ensemble average of CFSv2 is too small

indicating a systematic bias of CFSv2. When the CFSv2 raw forecasting is corrected by gamma function and multinomial

both Threat Score and the number of greatly increases but Bias Score increases in the meanwhile; ③ The relationship between SA and BMA can be expressed as follows: the 95th percentile of ensemble forecasting is used when SA indicates an abnormal precipitation

otherwise the 75th percentile is used.