High-resolution　data　of　fine　particulate　matters　(PM2.5)　are　of　great　interest　for　air　pollution　prevention　and　control.　However,　due　to　the　uneven　spatial　distribution　of　ground　stations,　satellite　acquisition　cycle,　and　cloud/rain,　high-resolution　products　cannot　be　provided　on　a　complete　spatio-temporal　scale.　To　provide　a　full　daily　PM2.5　product　in　recent　years　at　1-km　grid　of　the　Beijing-Tianjin-Hebei　(BTH)　region,　here　we　apply　a　state-ofthe-art　machine　learning　approach,　CatBoost,　to　(1)　reconstruct　satellite　aerosol　optical　depth　(AOD)　data;　and　to　(2)　estimate　gridded　PM2.5　from　station　measurements　combining　elevation,　meteorological　factors,　and　the　reconstructed　AOD　data.　Compared　with　existing　approaches,　CatBoost　substantially　improved　the　performance　of　AOD　reconstruction　by　similar　to　16%.　We　further　show　that　a　wavelet　decomposition　procedure　on　the　station-based　PM2.5　and　input　variables　is　helpful　to　improve　the　estimation　accuracy.　Overall,　the　approach　has　a　good　performance　in　estimating　PM2.5　with　a　cross-validation　R-2　of　0.88　and　root-mean-squared　error　of　17.79　mu　g/m(3).　From　the　new　dataset,　population-weighted　PM2.5　revealed　heterogeneous　spatial　distribution　of　exposure　in　different　areas,　consistently　higher　in　the　Southern　and　Eastern　BTH　and　lower　in　Beijing　and　Northern　BTH.　In　recent　years,　both　AOD　and　PM2.5　in　the　BTH　region　had　notable　interannual　decreases,　which　can　be　attributed　to　the　emission　reduction　efforts　and　interannual　natural　variabilities.