The quantification of PM(2.5) 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 PM(2.5) estimates. First, we calibrated CMAQ-based non-fire PM(2.5) to ground PM(2.5) observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM(2.5) concentrations in the second stage by multiplying the calibrated non-fire PM(2.5) obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM(2.5) during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM(2.5) better agreed with ground PM(2.5) observations with a 10-fold cross-validated (CV) R(2) of 0.79 compared to CMAQ-based PM(2.5) estimates with R(2) of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM(2.5) 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 PM(2.5) 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.