Abstract: In this study, the authors examine the impact of direct assimilation of FY-3D satellite MWHS II microwave humidity sounder data on extreme rainfall prediction, using the heavy rainstorm in Beijing on 31 July 2023, as a case study. Comparative experiments were conducted before and after data assimilation, and the effects across multiple scales and variables were analyzed using the WRF (Weather Research and Forecasting) numerical prediction model. The results show that MWHS II data assimilation considerably improved the extreme rainfall simulation. It successfully captured the maximum rainfall center, which exceeded 550 mm, and provided a more accurate simulation of rainfall distribution. This study also highlights the effect of assimilation on large-scale systems, thereby improving large-scale environmental fields by creating favorable conditions for extreme rainfall events. Key improvements include a strengthened temperature gradient in critical areas, optimized water vapor distribution, especially over the eastern sea, and an increased north–south pressure gradient. Together, these factors maintain a stable, large-scale background that supports precipitation. The impact on convective systems was more noticeable on a smaller scale. The vertical vorticity structure was optimized over the Beijing area, with enhanced negative vorticity in the mid-to-upper atmosphere and increased positive vorticity in lower levels, promoting upward motion. The atmosphere became more unstable, with increased relative humidity in the lower levels, decreased humidity in the mid-levels, and a steeper vertical temperature gradient. These factors contribute to strong convection triggering and maintenance. In addition, the microphysical processes improved. More snow and graupel particles formed in the mid-to-upper layers, and the conversion of cloud water to rainwater accelerated in lower levels, enhancing the overall precipitation efficiency. These effects were most prominent during the first 36 h of the simulation, emphasizing the critical role of data assimilation during the early and developing stages of precipitation.