Abstract: The radiance data of satellite microwave radiometers play a more and more important role in the assimilation of numerical prediction systems due to the all-sky advantage of satellite observations and the all-weather availability of microwave soundings. As an important category of passive microwave radiometer, the application potential of microwave imager in numerical weather forecasting needs further verification and more sufficient exploration. Focusing on the Advanced Microwave Scanning Radiometer 2 (AMSR2) carried on the Global Change Observation Mission–Water (GCOM-W), the thinning scheme within 200 km radius is applied; a quality control scheme including nine issues of checks is developed to screen those contaminations including the sun-glint phenomenon and radio-frequency interferences which can disturb the low-frequency channels; a bias correction scheme on the basis of conventional predictors is employed to effectively reduce systematic deviations of instrument; the difficulty related to observation error evaluation is overcome by posteriori verification. Ten channels of GCOM-W AMSR2 are directly assimilated into the Global/Regional Assimilation and Prediction System–Global Forecast System (CMA_GFS, i.e., the GRAPES_GFS) version 3.0 by the four-dimensional variational (4DVar) assimilation method independently developed by the Numerical Weather Prediction Center of China Meteorological Administrator. A pair of batch experiments for one month indicates that, with the GCOM-W AMSR2 assimilation, the depiction of humidity analysis field is improved, the medium forecasting skills of various precipitation are also improved, and the prediction score card shows obvious positive impacts on the southern hemisphere and the equatorial region. Thus, direct assimilation of the GCOM-W AMSR2 into the CMA_GFS 4DVar is confirmed to be useful for improving the amount of observations in poor-data regions, and it can take advantage of the water vapor sensitivity to promote the skills of humidity analysis and precipitation prediction.