2026/06/30 by Zihan Yang, Zhiqiang Gong, Ruowen Yang +5
Earth and Planetary Sciences · Environmental Science · #Climate variability and models #Meteorological Phenomena and Simulations #Precipitation Measurement and Analysis
paper · doi:10.1175/waf-d-25-0114.1
openalex publication_date 2026/06/30 · openalex created_date 2026/07/01 · openalex updated_date 2026/07/29
Abstract The Bayesian model averaging (BMA) method has gained considerable attention and application in the realm of precipitation prediction, particularly in ensemble and probabilistic predictions of precipitation. This study applies the BMA method to generate an ensemble prediction of summer precipitation over China, using forecasts from four dynamical climate models including ECMWFSEAS51, JMACPS3, NCCCSM11, and NCEPCFS2. We analyzed and evaluated the performance of the BMA ensemble prediction during 1991–2020, equal-weighted (EW) ensemble prediction, and individual model predictions based on the variables in terms of the spatial anomaly correlation coefficient (ACC), the prediction score (PS), and the root-mean-square errors (RMSEs). Additionally, we conducted an in-depth examination of the predictive uncertainty associated with each prediction. Primary findings are as follows: 1) Within the training period (1991–2014), the BMA ensemble prediction exhibited an average ACC of 0.12, surpassing both EW ensemble prediction and predictions of four individual models. The BMA ensemble prediction also achieved the highest PS score among all those approaches. 2) For the test period (2015–20), the BMA ensemble prediction consistently outperformed others with an average ACC and PS score of 0.21 and 78.3, respectively. Moreover, its RMSE value was consistently lower than that of competing predictions, further indicating the improved performance of BMA ensemble prediction. 3) BMA ensemble prediction result most closely aligned with the actual precipitation, as evidenced by the probability distribution of precipitation anomaly percentages. Notably, this outcome of BMA ensemble exhibited a superior signal-to-noise ratio compared to the other five predictions and demonstrated the least coefficient of variation among the precipitation predictions. Significance Statement The purpose of this study is to conduct multimodel ensemble precipitation forecasting through Bayesian model averaging (BMA) to improve forecast accuracy, as accurate precipitation prediction is essential for mitigating precipitation-related natural disasters. Our results show that the ensemble of model predictions by Bayesian averaging can further improve the accuracy of precipitation prediction and provide a feasible method for summer precipitation prediction in China.