Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

Introduction: Forest fires seriously threaten the safety of forest resources and human beings. Establishing an accurate forest fire forecasting model is crucial for forest fire management.

Methods: We used different meteorological and vegetation factors as predictors to construct forest fire prediction models for different fire prevention periods in Heilongjiang Province in northeast China. The logistic regression (LR) model, mixed-effect logistic (mixed LR) model, and geographically weighted logistic regression (GWLR) model were developed and evaluated respectively.

Results: The results showed that (1) the validation accuracies of the LR model were 77.25 and 81.76% in spring and autumn fire prevention periods, respectively. Compared with the LR model, both the mixed LR and GWLR models had significantly improved the fit and validated results, and the GWLR model performed best with an increase of 6.27 and 10.98%, respectively. (2) The three models were ranked as LR model < mixed LR model < GWLR model in predicting forest fire occurrence of Heilongjiang Province. The medium-and high-risk areas of forest fire predicted by the GWLR model were distributed in western and eastern parts of Heilongjiang Province in spring, and western part in autumn, which was consistent with the observed data. (3) Driving factors had strong temporal and spatial heterogeneities; different factors had different effects on forest fire occurrence in different time periods. The relationship between driving factors and forest fire occurrence varied from positive to negative correlations, whether it’s spring or autumn fire prevention period.

Discussion: The GWLR model has advantages in explaining the spatial variation of different factors and can provide more reliable forest fire predictions.