The interaction between temperature and rainfall determines the probability of tropical forest fire occurrence in Hainan Island

Severe forest fires have erupted in numerous tropical regions globally, threatening carbon storage in tropical ecosystems, the survival of plant species, and human health. Consequently, developing more precise prediction models for tropical forest fire hazards is essential for establishing effective fire prevention and management strategies. Although traditional logistic regression is widely employed for mapping forest fire probabilities, machine learning methods such as random forest have become more prevalent over the past decade. The applicability of random forest and logistic regression in predicting tropical forest fire probabilities has not been explored, leading to insufficient understanding of the driving factors of tropical forest fires on this tropical continental island with diverse forest types. This study integrated ground-based fire statistics from the Hainan Forestry Department and moderate resolution imaging spectroradiometer (MODIS) fire point data to create a highly accurate forest fire dataset for Hainan Island, spanning 20 years (2000–2020). Both logistic regression and random forest were used to develop tropical forest fire hazard models and explore the driving mechanisms of fires on Hainan Island. The results show that: (1) climatic factors contribute most significantly to the tropical forest fire probability, followed by human activities and topography, while vegetation factors (i.e., normalized difference vegetation index) made no significant contribution; (2) temperature and rainfall are the dominant factors influencing fire probability, with rising temperatures and decreasing rainfall substantially increasing the forest fire hazard; and (3) both logistic regression and random forest are reliable for predicting tropical forest fire hazards, but random forest demonstrates greater adaptability. In conclusion, our evidence suggests that the probability of tropical forest fires will increase under global warming and drought. The logistic regression and random forest models developed in this study provide valuable insights for identifying high-hazard forest fire areas in tropical regions. These findings have important implications for global tropical forest management and fire prevention, aiding in the formulation of targeted control strategies.