In this study, we compare the performance of the analysis time series over the period of August 2020 to December 2021 at EPA AirNow stations for both PM2.5 and O3 from raw Copernicus Atmosphere Monitoring Service (CAMS) reanalyses (CAMS RA Raw), raw CAMS near real-time forecasts (CAMS FC Raw), raw near real-time Community Multi-scale Air Quality (CMAQ) forecasts (CMAQ FC Raw), bias-corrected CAMS forecasts (CAMS FC BC), and bias-corrected CMAQ forecasts (CMAQ FC BC). This 17-month period spans two wildfire seasons, to assess model analysis performance in high-end AQ events. In addition to determining the best-performing gridded product, this process allows us to benchmark the performance of CMAQ forecasts against other global datasets (CAMS reanalysis and forecasts). For both PM2.5 and O3, the bias correction algorithm employed here greatly improved upon the raw model time series, and CMAQ FC BC was the best-performing model analysis time series, having the lowest RMSE, smallest bias error, and largest critical success index at multiple thresholds.