Rain is the main factor influencing the Ku-band scatterometer wind quality. The operational quality control (QC) scheme of the scatterometer wind retrieval procedure cannot effectively distinguish rain from high winds, limiting the optimal assimilation of Ku-band scatterometer winds in numerical weather prediction. To compensate for the inadequate operational QC of CFOSAT scatterometer (CSCAT) data, a real-time rain quality control (RRQC) method in data assimilation was proposed. Based on real-time rain forecasts, the RRQC method aims to screen and eliminate rain-affected CSCAT data. To begin, the impact of rain on CSCAT data for the entire year 2019 was analyzed. The CSCAT wind speed standard deviation (STD) and bias are found to increase with rain rate, and when the rain rate exceeds 2.5 mm/h, the STD is greater than the CSCAT-designed accuracy (2 m/s). Following that, two Typhoon cases, namely Lekima (2019) and In-fa (2021), were studied to assess the RRQC method's impact on Typhoon forecasting. It shows that assimilating CSCAT data with the RRQC method improves wind analysis and forecast, and reduces the mean track error. Finally, Typhoon Lekima was further diagnosed. It is found that the CSCAT data eliminated by RRQC are mainly distributed around the Lekima rainband with large wind speed observation minus background (OMB) absolute values. The results indicate that RRQC can identify rain contaminations in the CSCAT data and thus improve the analysis and forecast.