The techniques of bias correction are commonly used for improving the performance of deterministic air quality forecasting systems. One issue not addressed in previous studies is how to select systematically and objectively the best correction model from a pool of candidates. In this study, a method that could evaluate the probabilities of all model candidates based on a set of training data is proposed to select the most accurate and robust model by finding the one with the maximum probability. The Bayesian method was applied to select bias correction models at 12 monitoring stations for improving the forecasts of daily averaged PM₁₀ concentrations given by the deterministic air quality forecasting system WRF-EURAD in Porto, Portugal. At each station, 4095 (=2¹²–1) correction model candidates were systematically formed by adopting different linear combinations of 12 input variables. Selection of the best model was processed based on one year of monitoring and WRF-EURAD data. Based on the 2012 data, the selected model at each station was found to have significantly higher probability than the other candidates, and it is also much simpler than the full model. These selected models were then used to correct the raw forecasts by WRF-EURAD in the following year. The corrected forecasts show significant improvement on the performance indicators (RMSE by 35.8%, R by 58.5%, MFB by 68%, EDR by 38.3%, FAR by 51.8%, CSI by 30.8%) over the raw outputs of WRF-EURAD, confirming the success of the proposed technique.