Spatiotemporal Characteristics, Causes, and Prediction of Wildfires in North China: A Study Using Satellite, Reanalysis, and Climate Model Datasets

Understanding the characteristics of wildfires in North China is critical for advancing regional fire danger prediction and management strategies. This study employed satellite-based burned area products of the Global Fire Emissions Database (GFED) and reanalysis of climate datasets to investigate the spatiotemporal characteristics of wildfires, as well as their relationships with fire danger indices and climatic drivers. The results revealed distinct seasonal variability, with the maximum burned area extent and intensity occurring during the March–April period. Notably, the fine fuel moisture code (FFMC) demonstrated a stronger correlation with burned areas compared to other fire danger or climate indices, both in temporal series and spatial patterns. Further analysis through the self-organizing map (SOM) clustering of FFMC composites then revealed six distinct modes, with the SOM1 mode closely matching the spatial distribution of burned areas in North China. A trend analysis indicated a 7.75% 10a−1 (p < 0.05) increase in SOM1 occurrence frequency, associated with persistent high-pressure systems that suppress convective activity through (1) inhibited meridional water vapor transport and (2) reduced cloud condensation nuclei formation. These synoptic conditions created favorable conditions for the occurrence of wildfires. Finally, we developed a prediction model for burned areas, leveraging the strong correlation between the FFMC and burned areas. Both the SSP245 and SSP585 scenarios suggest an accelerated, increasing trend of burned areas in the future. These findings emphasize the importance of understanding the spatiotemporal characteristics and underlying causes of wildfires, providing critical insights for developing adaptive wildfire management frameworks in North China.