Forest fire is one of the most important disturbances in forest ecosystems, which significantly alters forest landscape structure and function worldwide. The spatial patterns of forest fire occurrence are closely associated with climate change. Researches have shown that fire frequency and burned area could substantially increase with prolonged growing seasons in warming climate scenarios. Revealing influences of climate change on the spatial distribution patterns of forest fires can provide scientific guidance for formulating feasible forest and fire management strategies. Therefore, based on the MODIS fire image (MCD14ML) data and 7 climatic (annual average temperature and precipitation), vegetation (forest type), topography (elevation) and human activities (population density and distances to the nearest roads and settlements) data from 2001 to 2005 in Jiangxi province, this study used the Boosted Regression Tree (BRT) model to:(1) quantify relationships, i.e., relative importance, marginal and incorporative effects, between fire occurrence and the explanatory variables; (2) project and generate fire occurrence maps under current (2001-2015) and two GCMs' (GFDL-CM3 and GISS-E2-R) future climate RCPs scenarios (RCP2.6, RCP4.5 and RCP8.5) in 2050 and 2070. We evaluated the performance of the BRT model using the area under curve (AUC) of a receiver operating characteristic curve (ROC). An alternative method to evaluate models was achieved by comparing the observed with the predicted fire occurrence with confusion matrixes method. The results showed that:(1) the annual temperature and altitude strongly correlated with the occurrence of forest fire in Jiangxi province, and the annual precipitation, distance to the residential areas, population density, and distance to roads had weaker correlations with the occurrence of forest fires; (2) the AUC values both of the training data (70%) and verification data (30%) were 0.736. The accuracy of the confusion matrix in predicting fire occurrence was 67.8%. The mod evaluation results indicated that the BRT model fitted well and could be used to predict forest fire occurrence in future climate scenarios in the study area; (3) the increasing in forest fire occurrence was the highest under the future climate scenario of RCP8.5; (4) fires would increase significantly in the cities of Ganzhou and Yingtan under future climate scenarios in both 2050 and 2070 compared with current climate scenarios (2001-2005). Forest and fire managers in Jiangxi province should strengthen their monitoring and management of forests in areas with high occurrence of forest fires based on the results from this study. And then they should strengthen public's awareness of fire prevention especially in southern Jiangxi province.