中文摘要：

针对两个最新换代的季度集合预测系统对中国季度降水预测中存在的系统缺陷，应用改进的贝叶斯联合概率模型（BJP）加以订正。对订正后的单一模式概率预测应用一种混合模型贝叶斯模型平均（BMA）方法加以集成，以综合各模式的优势来提高中国季度降水预测技巧。结果表明：BJP模型可有效地消除集合模式预测的系统偏差，同时大幅提高了概率预测的可靠性。经过订正的欧洲中尺度天气预报中心的System4预测在许多季度在中国的很大区域范围内都显示出了一定的预测技巧；而澳洲气象局的POAMA2．4预测只在个别季度局部范围内具有技巧。使用BMA对订正后的单一模式预测进行集成可显著提高对中国季度降水预测的精度，相比单一模式预测，技巧得分为正值的网格百分率分别提高了13．3％和20．0％。

英文摘要：

Abstract ：forecast systems in forecasting seasonal precipita- tly modified Bayesian joint probability （BJP） modelling approach was employed to calibrate the ensemble means of the raw forecasts firstly. The calibrated forecasts were then merged through Bayesian model av- eraging （BMA） to combine strengths from different models. The results suggested that the BJP calibration models ef- fectively removed biases and improved both reliability and overall accuracy of the raw forecasts. The calibrated ECMWF System4 （SYS4） forecasts exhibited some skill over broad regions of China in most seasons, whereas the cal- ibrated Australian Bureau of Meteorology＇s POAMA2.4 （P2.4） forecasts only showed weak skill over some regions in some seasons. Forecast skill of the merged forecasts from both sets of calibration models was improved greatly. Compa- ring with the SYS4 and P2.4 calibration forecast, the proportion of grid ceils with positive RMSEP skill score was im- proved by 13.3% and 20.0%, respectively.