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 μm) 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 μg/m3), 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 μg/m3) 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.