Abstract: To address the lack of the assimilation capability for Fengyun satellite microwave imager observations in the CMA-RA v1.5 development system and to enhance the application value of domestic satellite observations, this study processes L1 observations from the Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI), constructs an assimilation module based on the Gridpoint Statistical Interpolation (GSI) system, and performs a one-month batch experiment and evaluation using the Hybrid four-dimensional ensemble-variational (Hybrid-4DEnVar) assimilation method. The results show that the adopted quality control and bias correction schemes are reasonable and reliable, which can effectively screen high-quality clear-sky over-ocean observations and correct systematic biases. Assimilation of MWRI observations improves the quality of specific humidity analysis, i.e., the Root Mean Square Error (RMSE) of 800 hPa specific humidity analysis is reduced by up to 1.05% (passing the significance test), and the dry bias near 800 hPa in the tropics and the wet bias below 700 hPa in the southern Hemisphere are corrected. The 0—72 h specific humidity forecasts show significant improvements in the lower and middle troposphere (950—600 hPa), with particularly notable improvements occurring near 800 hPa during the first 24 h of forecast (the maximum error reduction reaches 8.74 mg/kg). The RMSEs of 3 h, 6 h, and 9 h forecasts are reduced by up to 1.4%, and the anomaly correlation coefficients of 700 hPa specific humidity forecasts are increased by 0.001—0.01. The forecasts of wind and temperature fields exhibit a neutral-to-positive effect, and the geopotential height forecast is improved at certain time steps of medium-range forecasts. The core contribution of FY-3D MWRI data assimilation focuses on the improvement of humidity analysis and forecasts, while its overall impact on other meteorological elements is relatively neutral.