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Abstract Previous research has examined individual factors contributing to wildfire risk, but the compounding effects of these factors remain underexplored. Here, we introduce the “Integrated Human-centric Wildfire Risk Index (IHWRI)” to quantify the compounding effects of fire-weather intensification and anthropogenic factors—including ignitions and human settlement into wildland—on wildfire risk. While climatic trends increased the frequency of high-risk fire-weather by 2.5-fold, the combination of this trend with wildland-urban interface expansion led to a 4.1-fold increase in the frequency of conditions conducive to extreme-impact wildfires from 1990 to 2022 across California. More than three-quarters of extreme-impact wildfires—defined as the top 20 largest, most destructive, or deadliest events on record—originated within 1 km from the wildland-urban interface. The deadliest and most destructive wildfires—90% of which were human-caused—primarily occurred in the fall, while the largest wildfires—56% of which were human-caused—mostly took place in the summer. By integrating human activity and climate change impacts, we provide a holistic understanding of human-centric wildfire risk, crucial for policy development. 

Abstract The area burned in the western United States during the 2020 fire season was the greatest in the modern era. Here we show that the number of human‐caused fires in 2020 also was elevated, nearly 20% higher than the 1992–2019 average. Although anomalously dry conditions enabled ignitions to spread and contributed to record area burned, these conditions alone do not explain the surge in the number of human‐caused ignitions. We argue that behavioral shifts aimed at curtailing the spread of COVID‐19 altered human‐environment interactions to favor increased ignitions. For example, the number of recreation‐caused wildfires during summer was 36% greater than the 1992–2019 average; this increase was likely a function of increased outdoor recreational activity in response to social distancing measures. We hypothesize that the combination of anomalously dry conditions and COVID‐19 social disruptions contributed to widespread increases in human‐caused ignitions, adding complexity to fire management efforts during the 2020 western US fire season. Knowledge of how social behavior changes indirectly contributed to the increased number of ignitions in the 2020 wildfire season can help inform resource management in an increasingly flammable world. 

Abstract BackgroundAs fire regimes are changing and wildfire disasters are becoming more frequent, the term megafire is increasingly used to describe impactful wildfires, under multiple meanings, both in academia and popular media. This has resulted in a highly ambiguous concept. ApproachWe analysed the use of the term ‘megafire’ in popular media to determine its origin, its developments over time, and its meaning in the public sphere. We subsequently discuss how relative the term ‘mega’ is, and put this in the context of an analysis of Portuguese and global data on fire size distribution. ResultsWe found that ‘megafire’ originated in the popular news media over 20 years before it appeared in science. Megafire is used in a diversity of languages, considers landscape fires as well as urban fires, and has a variety of meanings in addition to size. What constitutes ‘mega’ is relative and highly context‐dependent in space and time, given variation in landscape, climate, and anthropogenic controls, and as revealed in examples from the Netherlands, Portugal and the Global Fire Atlas. Moreover, fire size does not equate to fire impact. ConclusionGiven the diverse meanings of megafire in the popular media, we argue that redefining megafire in science potentially leads to greater disparity between science and practice. Megafire is widely used as an emotive term that is best left for popular media. For those wanting to use it in science, what constitutes a megafire should be defined by the context in which it is used, not by a metric of one‐size‐fits‐all. 

Abstract Lightning is a major source of wildfire ignition in the western United States (WUS). We build and train convolutional neural networks (CNNs) to predict the occurrence of cloud‐to‐ground (CG) lightning across the WUS during June–September from the spatial patterns of seven large‐scale meteorological variables from reanalysis (1995–2022). Individually trained CNN models at each 1° × 1° grid cell (n = 285 CNNs) show high skill at predicting CG lightning days across the WUS (median AUC = 0.8) and perform best in parts of the interior Southwest where summertime CG lightning is most common. Further, interannual correlation between observed and predicted CG lightning days is high (medianr = 0.87), demonstrating that locally trained CNNs realistically capture year‐to‐year variation in CG lightning activity across the WUS. We then use layer‐wise relevance propagation (LRP) to investigate the relevance of predictor variables to successful CG lightning prediction in each grid cell. Using maximum LRP values, our results show that two thermodynamic variables—ratio of surface moist static energy to free‐tropospheric saturation moist static energy, and the 700–500 hPa lapse rate—are the most relevant CG lightning predictors for 93%–96% of CNNs depending on the LRP variant used. As lightning is not directly simulated by global climate models, these CNNs could be used to parameterize CG lightning in climate models to assess changes in future CG lightning occurrence with projected climate change. Understanding changes in CG lightning risk and consequently lightning‐caused wildfire risk across the WUS could inform fire management, planning, and disaster preparedness. 

Abstract Cloud‐to‐ground lightning with minimal rainfall (“dry” lightning) is a major wildfire ignition source in the western United States (WUS). Although dry lightning is commonly defined as occurring with <2.5 mm of daily‐accumulated precipitation, a rigorous quantification of precipitation amounts concurrent with lightning‐ignited wildfires (LIWs) is lacking. We combine wildfire, lightning and precipitation data sets to quantify these ignition precipitation amounts across ecoprovinces of the WUS. The median precipitation for all LIWs is 2.8 mm but varies with vegetation and fire characteristics. “Holdover” fires not detected until 2–5 days following ignition occur with significantly higher precipitation (5.1 mm) compared to fires detected promptly after ignition (2.5 mm), and with cooler and wetter environmental conditions. Further, there is substantial variation in precipitation associated with promptly‐detected (1.7–4.6 mm) and holdover (3.0–7.7 mm) fires across ecoprovinces. Consequently, the widely‐used 2.5 mm threshold does not fully capture lightning ignition risk and incorporating ecoprovince‐specific precipitation amounts would better inform WUS wildfire prediction and management. 

Wildfire activity in the western United States has been on the rise since the mid-1980s, with longer, higher-risk fire seasons projected for the future. Prescribed burning mitigates the risk of extreme wildfire events, but such treatments are currently underutilized. Fire managers have cited lack of firefighter availability as a key barrier to prescribed burning. We use both principal component analysis (PCA) and logistic regression modeling methodologies to investigate whether or not (and if yes, under what conditions) personnel shortages on a given day are associated with lower odds of a prescribed burn occurring in the Okanogan–Wenatchee National Forest. We utilize the logit model to further assess how personnel availability compares to other potential barriers (e.g., meteorological conditions) in terms of association with odds of a prescribed burn occurring. Our analysis finds that fall and spring days in general have distinct constellations of characteristics. Unavailability of personnel is associated with lower odds of prescribed burning in the fall season, controlling for meteorological conditions. However, in the spring, only fuel moisture is observed to be associated with the odds of prescribed burning. Our findings suggest that if agencies aim to increase prescribed burning to mitigate wildfire risk, workforce decisions should prioritize firefighter availability in the fall. 

Some of the most catastrophic fire events that have occurred in the western US in recent decades, such as the 2018 Camp Fire in California, were ignited by electric utility infrastructure. As wildfires and fire seasons intensify across the western United States, policymakers and utilities alike are working to mitigate the risk of wildfire as it relates to utility infrastructure. We pose the following research question: Is there an association between risk factors such as wildfire hazard potential and social vulnerability, and the inclusion of various strategies in mitigation planning by public or cooperative electric utilities in Washington, such as PSPS provisions and non-expulsion fuse installation? By applying statistical tools including t-tests and logistic regression modeling to test these potential associations, our analysis reveals statistically significant relationships between risk factors and the inclusion of specific wildfire mitigation strategies. We find that the inclusion of PSPS provisions in mitigation planning is significantly and nonlinearly associated with wildfire hazard potential, while social and socioeconomic vulnerability in the utility service area are negatively associated. Additionally, the installation of non-expulsion fuses is negatively associated with socioeconomic vulnerability in service populations. Overall, understanding the factors associated with wildfire mitigation planning can assist policymakers and state agencies in the prioritization of resources and practical support for utilities that may have limited capacity to mitigate wildfire risk. 

Climate change increases fire-favorable weather in forests, but fire trends are also affected by multiple other controlling factors that are difficult to untangle. We use machine learning to systematically group forest ecoregions into 12 global forest pyromes, with each showing distinct sensitivities to climatic, human, and vegetation controls. This delineation revealed that rapidly increasing forest fire emissions in extratropical pyromes, linked to climate change, offset declining emissions in tropical pyromes during 2001 to 2023. Annual emissions tripled in one extratropical pyrome due to increases in fire-favorable weather, compounded by increased forest cover and productivity. This contributed to a 60% increase in forest fire carbon emissions from forest ecoregions globally. Our results highlight the increasing vulnerability of forests and their carbon stocks to fire disturbance under climate change. 

Background As fire seasons in the Western US intensify and lengthen, fire managers have been grappling with increases in simultaneous, significant incidents that compete for response resources and strain capacity of the current system. Aims To address this challenge, we explore a key research question: what precursors are associated with ignitions that evolve into incidents requiring high levels of response personnel? Methods We develop statistical models linking human, fire weather and fuels related factors with cumulative and peak personnel deployed. Key results Our analysis generates statistically significant models for personnel deployment based on precursors observable at the time and place of ignition. Conclusions We find that significant precursors for fire suppression resource deployment are location, fire weather, canopy cover, Wildland–Urban Interface category, and history of past fire. These results align partially with, but are distinct from, results of earlier research modelling expenditures related to suppression which include precursors such as total burned area which become observable only after an incident. Implications Understanding factors associated with both the natural system and the human system of decision-making that accompany high deployment fires supports holistic risk management given increasing simultaneity of ignitions and competition for resources for both fuel treatment and wildfire response.