China Water Resources

对2025年山东莱芜、北京密云、甘肃榆中、吉林集安4次极端暴雨事件进行复盘分析，指出当前数值预报模式在中小尺度对流系统的触发和生命周期预报中存在的局限性，尤其是对台风远距离水汽输送、复杂地形等因素引起的局地强降水事件的落区和强度预报都存在着较大偏差。针对这一挑战，从四个方面提出应对思路：强化水利测雨雷达组网建设应用，推进多源数据融合与AI反演技术，对降水进行更加准确的监测；升级“云—雨”降水模型，引入物理约束和动态参数校正机制，突破传统统计模型无法捕捉的多尺度降水系统瓶颈；构建台风残涡识别与预报模型，破解台风登陆后持续致灾问题；发展融合AI集合降水预报释用技术，量化极端降水事件发生概率。预期通过以上应对举措，能够延长致洪暴雨预见期，提高预报精准度，为防汛决策提供更有力的科学支撑。

A retrospective analysis was conducted on four extreme rainstorm events that occurred in Laiwu of Shandong Province, Miyun of Beijing, Yuzhong of Gansu Province, and Ji'an of Jilin Province in 2025. The analysis indicates that current numerical forecasting models still have evident limitations in predicting the initiation and life cycle of mesoscale and small-scale convective systems. In particular, large deviations remain in forecasting the location and intensity of localized heavy rainfall induced by long-distance moisture transport from typhoons and by complex topographic conditions. In response to these challenges, countermeasures are proposed from four aspects. First, the construction and application of water conservancy precipitation radar networks should be strengthened, while multi-source data fusion and AI-based inversion technologies should be advanced to achieve more accurate precipitation monitoring. Second, the “cloudrain” precipitation model should be upgraded by introducing physical constraints and dynamic parameter calibration mechanisms, so as to overcome the bottlenecks of traditional statistical models in capturing multi-scale precipitation systems. Third, identification and forecasting models for typhoon residual vortices should be developed to address the problem of sustained disaster impacts after typhoon landfall. Fourth, AI-integrated ensemble precipitation forecast interpretation techniques should be developed to quantify the occurrence probability of extreme precipitation events. Through the above measures, it is expected that the lead time of flood-causing rainstorms can be extended and forecast accuracy can be improved, thereby providing stronger scientific support for flood control decision-making.