Abstract: It's of immense importance to understand characteristics of dry and wet climate condition change in forest region of the Great Xing'an Mountains, and reveal its influence on forest fire pattern, which can provide scientific basis for forest fire management and forest resource protection in this region. Based on standardized precipitation index(SPI) in the Great Xing'an Mountains from 1974 to 2016, using statistical analysis and comparative analysis method, effects of different dry and wet scenarios on the number of forest fires and burned areas are systematically analyzed. And similarities and differences of different drought grade effects on forest fires are discussed. From 1974 to 2016, The annual climate of the Great Xing'an Mountains in Heilongjiang shows wetting trends, with several obvious stages. The annual fluctuation of SPI in seasonal scale is larger, and all of them show wetting trends. The precipitation in summer plays a decisive role in the change of annual dry-wet climate conditions. The forest fire frequency and burned areas are basically accordant with the grade of dry and wet climate. However, the number of forest fires is more closely related to the dry and wet climate condition. On annual scale, SPI value is negatively correlated with the number of fires, reaching 0.05 significant level. However, SPI value shows a weak negative correlation with the natural logarithm of the total burned areas, not passing the significant test. On seasonal scale, there is a significant negative correlation of SPI to the number of forest fires and the natural logarithm of burned areas. But the seasonal difference is great, and it's most significant in spring, followed by autumn, and relatively weak in summer. SPI in different seasons is negatively correlated with the number of annual forest fires and the natural logarithm of burned areas. Dry and wet climate has effects on the forest fires in lag period, and it's found that SPI in the previous winter contributes most to the number of forest fires. SPI can not only better reflect dry and wet conditions of regional climate, but also indicate the possibility of forest fire and the relative change of burned areas well. It can provide a scientific basis for forest fire prediction and management.