中文摘要：

用概括添加剂模型(鲸鱼群) 和平均的贝叶斯的模型(BMA ) 预报模型的概率的猛冲在这份报纸被建议。鲸鱼群被用来适合空间时间的降水模型到单个整体成员预报。降水出现和累积降水数量的分布被单个 Tweedie 分布同时代表。BMA 然后被用作一个 processing 以后方法联合单个模型形成一个更熟练的概率的预报模型。混合重量用期望最大化算法被估计。如果预报模型的适合的 BMA 充分捕获了降水的空间、时间的变化，剩余诊断被用来检验。建议方法用环境预言整体预报的国家中心为 2007 年 7 月在 Yishusi 河盆被用于每日的观察。由使用得分规则， BMA 预报被验证并且与实验概率的整体预报相比显示出更好的表演，特别地为极端降水。最后，到气候变化情形的 downscaling 的这个方法的可能的改进和应用被讨论。

英文摘要：

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a~plication of this method to the downscaling of climate change scenarios were discussed.