This paper predicts future changes in fire danger and the fire season in the current century for south-western China under two different climate change scenarios. The fire weather index (FWI) system calculated from daily outputs of a regional climate model with a horizontal resolution of 50 × 50 km was used to assess fire danger. Temperature and precipitation demonstrated a gradually increasing trend for the future. Forest fire statistics for 1987–2011 revealed that the FWI, initial spread index and seasonal severity rating were significantly related to the number of forest fires between 100 and 1000 ha in size. Over three future periods, the FWI component indices will increase greatly. The mean FWI value will increase by 0.83–1.85, 1.83–2.91 and 3.33–3.97 in the periods 2011–2040, 2041–2070 and 2071–2100. The regions with predicted FWI increases are mainly in central and south-eastern China. The fire season (including days with high, very high and extreme fire danger ratings) will be prolonged by 9–13, 18–21 and 28–31 days over these periods. This fire season extension will mainly be due to days with an extreme fire danger rating. Considering predicted future changes in the forest fire danger rating and the fire season, it is suggested that climate change adaptation measures be implemented.

Projecting the burn probability (BP) under future climate scenarios would provide a scientific basis for the implementation of forest fire adaptation technology. This study compared the changes in the climate, fire weather, and burn probability during the fire season in Daxing’anling, China. A burn probability model was established and used to simulate the daily fire occurrence and spread at baseline (1971–2000) and into the 2030s (2021–2050) based on the outputs from five global climate models (GCMs) (GFDL-ESM2M, Had GEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and Nor ESM1-M) under four climate scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). The results showed that the average daily maximum temperature in the fire season will be increased by 2.1 °C (+16.6%) in the 2030s compared with the baseline and precipitation in the fire season will be increased by 7.1%. The average fire weather index (FWI) of the fire season in the 2030s will be increased by 4.2%, but this change is not significant. There will be 39 fires per year in the 2030s, representing an increase of 11.4%. The accuracy of simulated burned areas was 71.2% for the 1991–2010 period. The simulated and observed burned areas showed similar interannual fluctuations during period 1971–2010. The potential burned areas in the 2030s will increase by 18.8% over those in the baseline period and the BP will increase by 19.4%. The implementation of proactive fire management in areas with high predicted BP values will be key for an effective mitigation of future wildfire impacts.

Background: Fire-caused tree mortality and spatial pattern are crucial for evaluating forest dynamics and developing management prescriptions. Aims: We investigated direct fire effects on spatial distribution and mortality of Dahurian larch (Larix gmelinii Rupr.) and assessed the Ryan and Amman (R–A) model performance and the importance of immediate mortality predictors. Methods: We analysed spatial patterns of fire-killed and surviving trees of three size classes in plots that burned at low- to high-severity using pair-correlation functions and tree mortality with the R–A model and generalised linear mixed models. Key results: The mixed-severity fire caused strong density-dependent mortality and more aggregated surviving tree patterns at short distances. The R–A model generally performed acceptably, and crown scorch and bole char height were critical predictors determining post-fire tree mortality. Conclusions: Fire-caused tree mortality and spatial patterns are controlled primarily by spatial variation in tree size and biological and structural characteristics. The prediction biases of the R–A model arose primarily from the intrinsic traits of Dahurian larch and the imbalanced dataset. Fine-scale neighbourhood density might be a fundamental priority for fire management and restoration. Implications: This study could possibly improve mechanistic understanding of spatial pattern development and tree mortality in similar fire-prone conifer forests.

Wildfires critically affect ecosystems, carbon cycles, and public health. COVID-19 restrictions provided a unique opportunity to study human activity’s role in wildfire regimes. This study presents a comprehensive evaluation of pandemic-induced wildfire regime changes across global fire-prone regions. Using MODIS data (2010–2022), we analyzed fire patterns during the pandemic (2020–2022) against pre-pandemic baselines. Key findings include: (a) A 22% global decline in wildfire hotspots during 2020–2022 compared to 2015–2019, with the most pronounced reduction occurring in 2022; (b) Contrasting regional trends: reduced fire activity in tropical zones versus intensified burning in boreal regions; (c) Stark national disparities, exemplified by Russia’s net increase of 59,990 hotspots versus Australia’s decrease of 60,380 in 2020; (d) Seasonal shifts characterized by December declines linked to mobility restrictions, while northern summer fires persisted due to climate-driven factors. Notably, although climatic factors predominantly govern fire regimes in northern latitudes, anthropogenic ignition sources such as agricultural burning and accidental fires substantially contribute to both fire incidence and associated emissions. The pandemic period demonstrated that while human activity restrictions reduced ignition sources in tropical regions, fire activity in boreal ecosystems during these years exhibited persistent correlations with climatic variables, reinforcing climate’s pivotal—though not exclusive—role in shaping fire regimes. This underscores the need for integrated wildfire management strategies that address both human and climatic factors through regionally tailored approaches. Future research should explore long-term shifts and adaptive management frameworks.

This study, based on multi-source data fusion and risk index models, has developed a comprehensive methodological system for evaluating the risk of forest fires caused by human factors. The system starts with four dimensions, i.e., exposure, hazard factors, vulnerability, and prevention and control capabilities, and constructs an evaluation framework with 19 secondary indicators. It also establishes single-category risk index models for four types of dominant fire sources: agricultural activities, religious ceremonies, tourism, and power distribution lines. Through weighted synthesis and exponential smoothing algorithms, it achieves daily dynamic risk forecasting. The research took the typical forest areas in the Mentougou, Changping, and Yanqing districts of Beijing as the application demonstration areas, collecting meteorological data, geographic information data, risk census ledgers, online hiking trajectories, and 2530 social survey questionnaires to complete the local parameter calibration and validation of the model. The retrospective analysis of 22 typical human-caused fire cases from 2018 to 2025 shows that the risk percentile of the ignition points in all cases was above 87.8%, indicating that the model has a good risk identification capability. Based on the evaluation results, differentiated control measures for different types of fire sources were proposed. The research results have been integrated into Beijing’s forest fire risk monitoring and early warning system, providing a scientific tool for the refined management of human-caused fire sources.