Abstract. Feng-Yun 3D (FY-3D) satellite is the latest polar-orbiting meteorological satellite launched by China and carry 10 instruments onboard. Its microwave temperature sounder (MWTS) and microwave humidity sounder (MWHS) can acquire a total of 28 channels of brightness temperatures, providing rich information for profiling atmospheric temperature and moisture. However, due to a lack of two important frequencies at 23.8 and 31.4 GHz, it is difficult to retrieve the total precipitable water vapor (TPW) and cloud liquid water path (CLW) from FY-3D microwave sounder data as commonly done for other microwave sounding instruments. Using the channel similarity between Suomi NPP advanced technology microwave sounder (ATMS) and FY-3D microwave temperature and humidity sounders, a machine learning technique is used to generate the two missing low frequency channels of MWTS and MWHS. Then, a new data set named as a combined microwave sounder (CMWS) is obtained and has the same channel setting as ATMS but the spatial resolution is consistent with MWTS. It is shown that the mean absolute errors of the two simulated channels are both between 3 and 4 K. The simulation errors mainly distribute in the high latitude regions, coastlines and the boundaries of some heavy rainfall. A statistical inversion method is adopted to retrieve TPW and CLW over oceans from the FY-3D CMWS. The inter-comparison between different satellites shows that the inversion products of FY-3D CMWS and Suomi NPP ATMS have good consistency in magnitude and distribution.