In　this　study,　a　hybrid　approach　of　combining　numerical　prediction　with　statistical　analysis　was　proposed　to　forecast　high-PM10　(aerosol　particle　with　aerodynamic　diameter　less　than　10　mu　m)　concentration　events　in　Beijing,　China.　This　approach　was　used　to　forecast　the　daily　PM10　in　Beijing　from　January　1　to　December　30,　2013.　The　WRF-CMAQ　modeling　system　was　also　applied　to　simulate　Beijing＇s　PM10　in　the　same　period.　The　performance　of　the　two　methods　was　then　assessed　according　to　the　mean　bias　(MB),　normalized　mean　bias　(NMB),　normalized　mean　gross　error　(NME),　mean　normalized　bias　(MNB),　mean　normalized　gross　error　(MNE),　and　root　mean　square　error　(RMSE).　The　results　demonstrate　that　both　methods　perform　well　during　low-PM10　concentration　periods　(PM10　concentration　＜　250　mu　g/m(3)),　the　MB,　NMB,　NME,　MNB,　MNE　and　RMSE　for　hybrid　approach　during　low-PM10　concentration　periods　were　26.15,　24.88%,　41.94%,　43.23%,　56.35%　and　61.67,　respectively.　The　MB,　NMB,　NME,　MNB,　MNE　and　RMSE　for　CMAQ　during　low-PM10　concentration　periods　were　-6.04,　57.47%,　41.49%,　21.52%,　55.64%　and　60.11,　respectively.　While　the　MB,　NMB,　NME,　MNB,　MNE　and　RMSE　for　CMAQ　during　high-PM10　concentration　periods　(PM10　concentration　＞=　250　mu　g/m(3))　were　-162.87,　-50.37%,　50.37%,　-49.86%,　49.86%　and　175.93,　respectively.　The　MB,　NMB,　NME,　MNB,　MNE　and　RMSE　for　hybrid　approach　during　high-PM10　concentration　periods　were　-30.3,　-9.37%,　23.21%,　-8.21%,　24.25%　and　97.37,　respectively.　The　hybrid　approach　shows　significant　improvement　in　accuracy　during　high-PM10　concentration　periods.