Abstract: A new thunderstorm prediction method, namely, Binary Particle Swarm Optimization-Bayes Discriminatory Criterion (BPSO-BDC) was proposed, which can use Binary Particle Swarm Optimization (BPSO) algorithm to automatically screen the optimal subset of Bayes Discriminatory Criterion (BDC) model, overcoming the shortcomings of BDC model in factor selection. In order to establish the optimal model of BDC thunderstorm, the BDC thunderstorm forecasting model for three stations at Zhangzhou, Yiwu and Ledong was studied, by using T511 numeral prediction product and single-station observation data of 2010-2014. By selecting the fitness function, the optimal subset of BPSO-BDC model is proposed, and the optimal subset model for the three stations is obtained and compared with BDC and stepwise discriminant model. Results show that for thunderstorm prediction through equation established by BPSO-BDC method, in the 24h thunderstorm forecast, its mean value of threat score reaches 0.697, mean value of the false alarm rate is 0.256, and mean value of the missing alarm rate is 0.048. In the 48h thunderstorm forecast, its mean value of threat score reaches 0.418, mean value of the false alarm rate is 0.222. Results of BPSO-BDC model is obviously better than BDC and stepwise discriminant model. The results of the BPSO-BDC model showed that the TS scores were between 0.21 and 0.35, and the variation range was small and at a high level. Prediction effect of BPSO-BDC model is obviously better than BDC and stepwise discriminant, with good stability and prediction capability.