Fengyun-3D (FY-3D) satellite is the latest polar-orbiting meteorological satellite launched by China and carries 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 tem-perature and moisture. However, due to a lack of two important frequencies at 23.8 and 31.4 GHz, it is difficult to re-trieve 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 Na-tional Polar-orbiting Partnership (NPP) advanced technology microwave sounder (ATMS) and FY-3D microwave sounding instruments, a machine learning (ML) technique is used to generate the two missing low-frequency chan-nels of MWTS and MWHS. Then, a new dataset named as combined microwave sounder (CMWS) is obtained, which has the same channel setting as ATMS but the spatial resolution is consistent with MWTS. A statistical inver-sion method is adopted to retrieve TPW and CLW over oceans from the FY-3D CMWS. The intercomparison between different satellites shows that the inversion products of FY-3D CMWS and Suomi NPP ATMS have good consistency in magnitude and distribution. The correlation coefficients of retrieved TPW and CLW between CMWS and ATMS can reach 0.95 and 0.85, respectively.