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定量降水预报是无缝隙精细化网格预报中最具挑战的部分,目前存在需要长时间序列的训练样本、大多基于单模式订正及局地偏差特征反应不足等问题.本文提出基于相似网格点的多源定量降水预报融合算法以解决上述问题.该算法融合多家模式6小时降水预报产品生成预报产品.该融合算法可分为模式偏差订正、动态权重融合和削空后处理三步.其中模式偏差订正采用分位映射法,使用相似网格点(supplemental grid points)扩充用于建模的样本总量:相似网格点考虑了网格点之间地形、空间距离和降水气候特征之间的相似度.动态权重融合基于预报的TS评分更新动态融合权重.融合后使用弱降水削空对融合结果进行后处理.弱降水削空采用概率预报的思想,当参与融合的模式中,有超过40％的模式认为该网格点有降水时,才在该点预报降水.对2019年4-10月检验结果表明,多模式融合算法可有效改进各量级降水的TS(Treat Score)和Bias评分;对暴雨量级(25mm阈值)的TS提升率可达20-40％,其中24小时时效相较ECMWF模式提升47.6％;同时,融合方案Bias评分更接近于1,优于单模式预报.“,”Quantitative Precipitation Forecast (QPF) is a challenging issue in seamless prediction. QPF faces the following difficulties: (i) single rather than multiple model products are still used; (ⅰ) most QPF methods require long-term training samples not easily available, and (ⅱ) local features are insufficiently reflected. In this work, a multi-model blending (MMB) algorithm with supplemental grid points (SGPs) is experimented to overcome these shortcomings. The MMB algorithm includes three steps: (1) single-model bias-correction, (2) dynamic weight MMB, and (3) light-precipitation elimination. In step 1, quantile mapping (QM) is used and SGPs are configured to expand the sample size. The SGPs are chosen based on similarity of topography, spatial distance, and climatic characteristics of local precipitation. In step 2, the dynamic weight MMB uses the idea of ensemble forecasting: a precipitation process can be forecast if more than 40％ of the models predict such a case; moreover, threat score (TS) is used to update the weights of ensemble members. Finally, in step 3, the number of false alarms of light precipitation is reduced, thus al-leviating unreasonable expansion of the precipitation area caused by the blending of multiple models. Verification results show that using the MMB algorithm has effectively improved the TS and bias score (BS) for blended 6-h QPF. The rate of increase in TS for heavy rainfall (25-mm threshold) reaches 20％?40％; in particular, the improvement has reached 47.6％ for forecast lead time of 24 h, compared with the ECMWF model. Meanwhile, the BS is closer to 1, which is better than any single-model forecast. In sum, the QPF using MMB with SGPs shows great potential to further improve the present operational QPF in China.