利用基于历史资料的模式距平积分预报订正（ANO）方法，结合欧洲中期天气预报中心第五代（ERA5）全球再分析资料，对中国西南区域数值天气预报模式系统（简称 SWMS）预报产品进行订正试验，检验了ANO方法对2019年6月20-25日一次复杂地形条件下灾害性区域暴雨过程气象要素的中短期天气预报订正改进效果。结果发现，SWMS模式在对流层中上层的预报性能较好，但在中低层随着高度降低预报质量逐渐下降，经 ANO方法订正后各预报变量均有所改进，前 72 h预报 500 hPa和 700 hPa位势高度场的平均距平相关系数（ACC）提高 0. 1~0. 2，达到 0. 8左右，而 850 hPa的位势高度 ACC 最大提高0. 6；同时，700 hPa 和850 hPa订正位势高度的均方根误差（RMSE）降低显著，分别平均降低了24%和66%。温、湿、风场的订正效果也显示出很好的正效果，展示了基于历史资料的ANO方法对我国西南复杂地形区域中短期数值预报的订正能力和提高SWMS模式数值预报技巧的有效性。

Utilizing the Anomaly Numerical-correction with Observations（ANO）based on historical observation data and anomaly integration，in conjunction with the ERA5 reanalysis data，a rectification test was conduct‐ ed on the forecasting products of the Southwest Center WRF-ADAS Real-time Modeling System（SWMS in short）. This study evaluated the efficiency of the ANO method in enhancing short- to medium-term weather fore‐ casts for meteorological quantities during a catastrophic regional heavy rainfall event over the complex topography from June 20 to 25，2019. Results revealed that the SWMS model exhibited commendable predictability in the middle and upper troposphere，although its accuracy gradually reduced in the lower layers. After post-processing correction using the ANO method，all the predicted variables showed obvious improvements. The aver‐ age Anomaly Correlation Coefficient（ACC）for the 500 hPa and 700 hPa geopotential height fields within the 72-hour integration increased by a range of 0. 1 to 0. 2，reaching approximately 0. 8，while the 850 hPa geopotential height ACC showed a maximum enhancement of 0. 6. Concurrently，the Root Mean Square Error （RMSE）for the corrected 700 hPa and 850 hPa geopotential heights exhibited significant reductions with an average decrease of 24% and 66%，respectively. The correction outcomes for temperature，specific humidity，and horizontal wind also displayed positive effects，which reveal the efficient correcting performance of the ANO method based on historical observation data in rectifying short- to medium-term numerical forecasts of the SWMS model over complex topography.