The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM2.5) from wildfires are limited by the lack of accurate high‐resolution PM2.5exposure data over fire days. Satellite‐based aerosol optical depth (AOD) data can provide additional information in ground PM2.5concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM2.5concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi‐angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM2.5concentrations over fire seasons (April to September) in Colorado for 2011–2014. Our model had a 10‐fold cross‐validated R2of 0.66 and root‐mean‐squared error of 2.00 μg/m3, outperformed the multistage model, especially on the fire days. Elevated PM2.5concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short‐term and long‐term epidemiological studies of wildfire PM2.5. A Bayesian ensemble model was used to predict the daily PM2.5concentrations during fire seasons in ColoradoOur model successfully captured the PM2.5enhancements over large fire eventsThe data sets obtained in this study could support future epidemiological studies of wildfire PM2.5in Colorado