The quantification of PM2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as ‘fire’) is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM2.5 estimates. First, we calibrated CMAQ-based non-fire PM2.5 to ground PM2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM2.5 concentrations in the second stage by multiplying the calibrated non-fire PM2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM2.5 better agreed with ground PM2.5 observations with a 10-fold cross-validated (CV) R2 of 0.79 compared to CMAQ-based PM2.5 estimates with R2 of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95 % confidence interval (CI) = 1.021–1.130 in October; RR = 1.191, 95%CI = 1.099–1.291 in November; RR for respiratory disease = 1.078, 95%CI = 1.005–1.157 in October; RR = 1.153, 95%CI = 1.045–1.272 in November. However, the results were inconsistent when non-calibrated PM2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.