Fine　particulate　matter　with　aerodynamic　diameters　less　than　2.5　μm　(PM2.5)　poses　adverse　impacts　on　public　health　and　the　environment.　It　is　still　a　great　challenge　to　estimate　high-resolution　PM2.5　concentrations　at　moderate　scales.　The　current　study　calibrated　PM2.5　concentrations　at　a　1　km　resolution　scale　using　ground-level　monitoring　data,　Aerosol　Optical　Depth　(AOD),　meteorological　data,　and　auxiliary　data　via　Random　Forest　(RF)　model　across　China　in　2017.　The　three　ten-folded　cross-validations　(CV)　methods　including　sample-based,　time-based,　and　spatial-based　validation　combined　with　Coefficient　Square　(R2),　Root-Mean-Square　Error　(RMSE),　and　Mean　Predictive　Error　(MPE)　have　been　used　for　validation　at　different　temporal　scales　in　terms　of　daily,　monthly,　heating　seasonal,　and　non-heating　seasonal.　Finally,　the　distribution　map　of　PM2.5　concentrations　was　illustrated　based　on　the　RF　model.　Some　findings　were　achieved.　The　RF　model　performed　well,　with　a　relatively　high　sample-based　cross-validation　R2　of　0.74,　a　low　RMSE　of　16.29　μg　×　m−3,　and　a　small　MPE　of　−0.282　μg　×　m−3.　Meanwhile,　the　performance　of　the　RF　model　in　inferring　the　PM2.5　concentrations　was　well　at　urban　scales　except　for　Chengyu　(CY).　North　China,　the　CY　urban　agglomeration,　and　the　northwest　of　China　exhibited　relatively　high　PM2.5　pollution　features,　especially　in　the　heating　season.　The　robustness　of　the　RF　model　in　the　present　study　outperformed　most　statistical　regression　models　for　calibrating　PM2.5　concentrations.　The　outcomes　can　supply　an　up-to-date　scientific　dataset　for　epidemiological　and　air　pollutants　exposure　risk　studies　across　China.　©　2021