| Summary: | An accurate forecast of fine particulate matter (PM<sub>2.5</sub>) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM<sub>2.5</sub> pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM<sub>2.5</sub> forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM<sub>2.5</sub> concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM<sub>2.5</sub> concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM<sub>2.5</sub> forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM<sub>2.5</sub> forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m<sup>3</sup>, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM<sub>2.5</sub>. Moreover, the data accuracy varied between seasons, as accurate PM<sub>2.5</sub> forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM<sub>2.5</sub> forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM<sub>2.5</sub> forecasts could be used as a promising data source to prevent general public from severe PM<sub>2.5</sub> pollution events given the improved data accuracy.
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