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Wildfire-released greenhouse gases and aerosols accelerate global warming and exacerbate air pollution, substantially altering atmospheric composition and cloud microphysical properties. These alterations directly influence the formation of cloud-to-ground (CG) lightning and the subsequent regime of lightning-ignited wildfires (LIWs). However, significant knowledge gaps persist regarding these processes in boreal forests, the world’s largest and most LIW-prone biome. This study integrates high-resolution datasets on CG lightning, aerosol loading, LIWs, and meteorology from the Greater Khingan Mountains forests in Northeast China to elucidate their interconnections. The results indicate that LIW emissions are associated with pronounced spatiotemporal variations in aerosol loading, and that elevated aerosol levels, dominated by sulfate, coincide with enhanced CG lightning occurrence. This phenomenon is likely modulated by aerosol–cloud–convection interactions, whereby aerosols modify cloud microphysics and intensify atmospheric electrification. Furthermore, high aerosol loading co-occurs with near-surface meteorological conditions conducive to LIW ignition and spread, suggesting a potential positive feedback mechanism. This hypothesized cycle, linking wildfire emissions, CG lightning, and subsequent ignitions, may become self-reinforcing under a warming climate.

This work studies the characteristics of thunderstorms that cause lightning-caused wildfires in Catalonia, north-east Iberian Peninsula, using lightning and weather radar data. Although thunderstorms produce ~57 000 cloud-to-ground (CG) flashes yearly in Catalonia, only 1 in 1000 end up as a flaming wildfire. Characterisation of thunderstorms that ignite wildland fires can help fire weather forecasters identify regions of increased ignition potential. Lightning data and radar products like maximum reflectivity, echo tops heights and equivalent liquid content were obtained over a 7-year period. Characteristics of thunderstorms that ignite wildfires are examined including storm motion, duration, morphology and intensity. It was found that most probable ignition candidates are lightning associated with cellular thunderstorms and non-linear systems. Radar reflectivity values for lightning that ignites wildfires were found to be below average, these morphological types favouring the occurrence of lightning outside regions of high reflectivity, where precipitation reaching the ground is low or non-existent. Thunderstorms that ignite wildfires are typically of low intensity, with a CG flash rate below average. Most ignitions occur during the maturity phase when the CG flash rate is the highest. A better scientific understanding of the thunderstorms that cause lightning wildfires will help improve early firefighting response.

Wildland fires exert substantial impacts on tundra ecosystems of the high northern latitudes (HNL), ranging from biogeochemical impact on climate system to habitat suitability for various species. Cloud-to-ground (CG) lightning is the primary ignition source of wildfires. It is critical to understand mechanisms and factors driving lightning strikes in this cold, treeless environment to support operational modeling and forecasting of fire activity. Existing studies on lightning strikes primarily focus on Alaskan and Canadian boreal forests where land-atmospheric interactions are different and, thus, not likely to represent tundra conditions. In this study, we designed an empirical-dynamical method integrating Weather Research and Forecast (WRF) simulation and machine learning algorithm to model the probability of lightning strikes across Alaskan tundra between 2001 and 2017. We recommended using Thompson 2-moment and Mellor-Yamada-Janjic schemes as microphysics and planetary boundary layer parameterizations for WRF simulations in the tundra. Our modeling and forecasting test results have shown a strong capability to predict CG lightning probability in Alaskan tundra, with the values of area under the receiver operator characteristics curves above 0.9. We found that parcel lifted index and vertical profiles of atmospheric variables, including geopotential height, dew point temperature, relative humidity, and velocity speed, important in predicting lightning occurrence, suggesting the key role of convection in lightning formation in the tundra. Our method can be applied to data-scarce regions and support future studies of fire potential in the HNL.

Lightning-ignited wildfires represent a dominant natural disturbance agent in the Greater Khingan Mountains of northeastern China; however, the relationship between their occurrence and lightning characteristics remains insufficiently quantified. This study analyzed cloud-to-ground (CG) lightning data (2019–2024) and 417 lightning-ignited wildfires (2019–2024) using a full-waveform lightning detection network and spatial matching based on the Minimum Distance Method. Lightning activity shows pronounced spatiotemporal clustering, with more than 93% of flashes occurring in summer and a diurnal peak at 15:00. About 74.6% of wildfires ignited within 1 km of a lightning strike, and the holdover time exhibited clear seasonality, peaking in August (≈317 h). Negative CG (−CG) flashes dominated ignition events (56.5% multiple-stroke, average multiplicity = 2.60), and igniting flashes were concentrated within the −10 to −30 kA peak-current range, suggesting a key threshold for ignition. Vegetation type strongly influenced ignition efficiency: cold temperate and temperate coniferous forests recorded the highest lightning and fire counts, while alpine grasslands and sedge meadows showed the highest lightning ignition efficiency (LIE). These findings clarify how lightning electrical properties and vegetation conditions jointly determine ignition probability and provide a scientific basis for improving lightning-ignited wildfire risk monitoring and early-warning systems in boreal forest regions.

Lightning-ignited wildfires (LIWs), which predominantly occur in remote regions, pose significant challenges for prediction and mitigation, while contributing disproportionately to burned areas in Texas. Persistent knowledge gaps regarding key ignition drivers and their nonlinear interdependencies hinder the development of targeted prevention strategies and robust early warning systems. To address these limitations, we compiled a statewide dataset spanning 2010–2020 comprising 4775 LIWs and employed an optimized repeated-random undersampling strategy to mitigate class imbalance. Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. The most accurate classifier achieved an out-of-sample prediction accuracy of 85.81%, outperforming the fire weather index and methodologies of random forests and logistic regression. Key drivers of ignition were identified as higher lightning frequency, elevated temperatures, and lower fuel moisture content. Spatial analysis revealed LIW clustering in needleleaf forests of eastern Texas, where maximum fuel loading (indicated by normalized difference vegetation index) and lightning density interacted to create ignition-prone conditions.

The analysis of the spatio-temporal variability of lightning-ignited wildfires and meteorological conditions preceding their occurrence from both dry lightning and lightning with precipitation in Western Siberia for the warm seasons (May–September) of 2016–2021 was carried out. In the Arctic zone, fires from lightnings occur in most cases (83%) almost without precipitation (<2.5 mm/day), whereas in the forest and steppe zones the number of cases is less (81% and 74%, respectively). The most significant changes in meteorological conditions before the ignition were also revealed in the northern part 3–4 days before. Among all considered parameters, the most important role in the occurrence of dry lightning-ignited wildfires belongs to mid-tropospheric instability, lower-tropospheric dryness, and the moisture content of the top soil and surface floor layer. Moreover, in the Arctic zone of Western Siberia, more extreme (hotter and drier) meteorological conditions should be observed for the occurrence of ignition from lightning. The threshold values for the considered meteorological parameters were derived for our region for the first time. Obtained results can be used in the development of models for potential fire hazards prediction in various landscapes, which will have a practical application in various spheres of the national economy.

Lightning is the main precursor of wildfires in Arizona, New Mexico, and Florida during the fire season. Forecasting the occurrence of Lightning-Ignited Wildfires (LIW) is an essential tool to reduce their impacts on the environment and society. Long-Continuing-Current (LCC) lightning is proposed to be the main precursor of LIW. The long-lasting continuing current phase of LCC lightning is that which is more likely to ignite vegetation. We investigated the meteorological conditions and vegetation type associated with LIW and LCC lightning flashes in Arizona, New Mexico, and Florida. We analyzed LIW between 2009 and 2013 and LCC lightning between 1998 and 2014 and combined lightning and meteorological data from a reanalysis data set. According to our results, LIW tend to occur during dry thunderstorms with a high surface temperature and a high temperature gradient between the 700 hPa and the 450 hPa vertical levels for high-based clouds. In turn, we obtained a high lightning-ignition efficiency in coniferous forests, such as the ponderosa pine in Arizona and New Mexico and the slash pine in Florida. We found that the meteorological conditions that favor fire ignition and spread are more significant in Florida than in Arizona and New Mexico, while the meteorological conditions that favor the occurrence of LIW in Arizona and New Mexico are closely related with the meteorological conditions that favor high lightning activity. In turn, our results indicate high atmospheric instability during the occurrence of LIW. Our findings suggest that LCC (>18 ms) lightning tends to occur in thunderstorms with high relative humidity and ice content in the clouds, and with low temperature in the entire troposphere. Additionally, a weak updraft in the lower troposphere and a strong one in the upper troposphere favor the occurrence of LCC (>18 ms) lightning. We found that the meteorological conditions that favor the occurrence of LCC (>18 ms) lightning are not necessarily the preferential meteorological conditions for LIW.

In the climate change context, wildfires are an increasing hazard in the Mediterranean Basin, especially those triggered by lightning. Although lightning activity can be predicted with a reasonable level of confidence, the challenge remains in forecasting the thunderstorm’s probability of ignition. The present work aims to characterise the most suitable predictors to forecast lightning-ignited wildfires. Several ERA5 parameters were calculated and compared for two different samples, thunderstorm episodes that caused a wildfire (n = 961) and ordinary thunderstorms (n = 1023) that occurred in Catalonia (NE Iberian Peninsula) in the 2006–2020 period. Lightning wildfires are mostly associated with dry thunderstorms, characterised by: weak-to-moderate Mixed-Layer Convective Available Potential Energy (MLCAPE, 150–1100 J kg−1), significant Dew Point Depression at 850 hPa (DPD850, 3.3–10.1 °C), high Most-Unstable Lifted Condensation Level (MULCL, 580–1450 m) and steep 500–700 hPa Lapse Rate (LR, −7.0–−6.3 °C). Under these conditions, with relatively dry air at lower levels, thunderstorms tend to be high-based, the rain evaporating before reaching the ground and lightning occurring without significant rainfall. Specifically forecasting the probability of LIW occurrence would be of great assistance to the forest protection tactical decision-making process, preparing for “dry” thunderstorm days where multiple ignitions can be expected.

Background Over the last four decades, wildfires in forests of the continental western United States have significantly increased in both size and severity after more than a century of fire suppression and exclusion. Many of these forests historically experienced frequent fire and were fuel limited. To date, fuel reduction treatments have been small and too widely dispersed to have impacted this trend. Currently new land management plans are being developed on most of the 154 National Forests that will guide and support on the ground management practices for the next 15–20 years. Results During plan development, we recommend that Strategic Fire Zones (SFZs) be identified in large blocks (≥ 2,000 ha) of Federal forest lands, buffered (≥ 1–2.4 km) from the wildland-urban interface for the reintroduction of beneficial fire. In SFZs, lightning ignitions, as well as prescribed and cultural burns, would be used to reduce fuels and restore ecosystem services. Although such Zones have been successfully established in a limited number of western National Parks and Wilderness Areas, we identify extensive remote areas in the western US (8.3–12.7 million ha), most outside of wilderness (85–88%), where they could be established. Potential wildland fire Operational Delineations or PODs would be used to identify SFZ boundaries. We outline steps to identify, implement, monitor, and communicate the use and benefits of SFZs. Conclusions Enhancing collaboration and knowledge-sharing with Indigenous communities can play a vital role in gaining agency and public support for SFZs, and in building a narrative for how to rebuild climate-adapted fire regimes and live within them. Meaningful increases in wildland fire use could multiply the amount of beneficial fire on the landscape while reducing the risk of large wildfires and their impacts on structures and ecosystem services.

The Brazilian Amazon contains approximately 40% of the world’s tropical rainforest and plays a critical role in preserving biodiversity and regulating water, energy and carbon cycles. However, deforestation and increasingly frequent droughts, heatwaves and wildfires threaten these rainforests. Amazonian fires are generally assumed to be entirely anthropogenic, which has led to lightning-ignited fires being underexplored. Here, we present the first detailed assessment of the spatiotemporal patterns of lightning-ignited fires in the Amazon rainforest to elucidate the role of lightning and human ignitions in shaping Amazon fire dynamics. To do this, we matched cloud-to-ground lightning strokes from the Global Lightning Dataset (GLD360) with individual fire events between 2019 and 2024 to obtain a probability of lightning ignition for each fire. We also calculated a human-ignition probability index using proximity to roads, waterways, and human land cover as proxies for human activity. By combining both probabilistic indices with ground-observed lightning ignitions from eight protected areas, we could optimize the threshold that determines if an ignition is more likely to be caused by lightning or human activities. We estimate that in the Brazilian Amazon, lightning caused on average 0.2%–0.4% of all fires each year (234–407 ignitions per year) and 1.1%–1.2% of the annually burned area (1226–1358 km2 per year) between 2019 and 2024. More than 89% of these fires occurred in the late dry season between August and November, peaking in September and October. Despite lightning-ignited fires contributing a small proportion of all Amazonian fires, they constitute over 25% of the fires in identified grid clusters in parts of the states of Pará (particularly in the Breves region), Amazonas, and Rondônia. This study provides the first estimation of the role of natural ignitions in Amazon fire dynamics and a scientific basis for understanding their contribution within the region.