Abstract: Incorporating cloud condensation information into the initial field of mesoscale weather predicting models is crucial yet challenging. In this study, we utilized the local analysis and prediction system (LAPS) to integrate data from the FY-4A advanced geostationary radiation imager (AGRI) satellite, specifically its visible light, water vapor, and infrared channels, alongside 3D radar reflectivity. This approach allowed us to obtain detailed mapping of cloud distributions on macro and micro scales. Hydrometeors within cloud distributions can be further ingested into the initial field to trigger a mesoscale model warm start. In this study, we employed the LAPS to ingest data focusing on a Mei-yu event from July 1 to 8, 2020. This paper compared cloud analysis products and precipitation predictions both with and without the inclusion of FY-4A AGRI satellite-ingested data. When compared with the MODIS cloud product, our results showed that incorporating FY-4A AGRI data enhances the accuracy of cloud top height and cloud horizontal distribution. The initial field enriched with FY-4A AGRI data aids in eliminating inaccuracies, specifically the false centers of hydrometers. Further comparison with ERA5 hourly hydrometeor revealed that utilizing FY-4A AGRI LAPS data diminishes the peaks of cloud ice and cloud water mixing ratios and eradicates the false centers of hydrometeors. Forecasts made with LAPS ingested initial field outperformed others in the 24-hour cumulative precipitation equitable threat scores and bias scores. The fractions skill score (FSS) of hourly precipitation prediction with LAPS ingested initial field can also be improved in the first hour.