It is challenging to retrieve hourly ground-level PM_2.5 on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM_2.5 of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data. A one-year PM_2.5 retrieval shows a strong agreement to ground-based measurements, with the averaged R² approaching 0.92, while the RMSE was only 10.0 μg/m³. An analysis of the regional differences of the performance and the dependency on satellite Viewing Zenith Angle (VZA) show that sparse measurements, high VZA, and solar zenith angle (SZA) are the primary sources of the uncertainty. The use of the FY-4A improved 17% spatial coverage compared to the Himawari-8-based PM_2.5 retrievals, enabling full-coverage, hourly PM_2.5 monitoring over China, and potentially could improve PM_2.5 predictions from air quality models after data assimilation.