Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km² (25.3%) of the Earth’s vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km² (53.4%) during the second half of the study period. If these fire weather changes are coupled with ignition sources and available fuel, they could markedly impact global ecosystems, societies, economies and climate.

We explore the basis of understanding wildland fire behaviour with the intention of stimulating curiosity and promoting fundamental investigations of fire spread problems that persist even in the presence of tremendous modelling advances. Internationally, many fire models have been developed based on a variety of assumptions and expressions for the fundamental heat transfer and combustion processes. The diversity of these assumptions raises the question as to whether the absence of a sound and coherent fire spread theory is partly responsible. We explore the thesis that, without a common understanding of what processes occur and how they occur, model reliability cannot be confirmed. A theory is defined as a collection of logically connected hypotheses that provide a coherent explanation of some aspect of reality. Models implement theory for a particular purpose, including hypotheses of phenomena and practical uses, such as prediction. We emphasise the need for theory and demonstrate the difference between theory and modelling. Increasingly sophisticated fire management requires modelling capabilities well beyond the fundamental basis of current models. These capabilities can only be met with fundamental fire behaviour research. Furthermore, possibilities as well as limitations for modelling may not be known or knowable without first having the theory.

Particularly in rural settings, there has been little research regarding the health impacts of fine particulate matter (PM_2.5) during the wildfire season smoke exposure period on respiratory diseases, such as influenza, and their associated outbreaks months later. We examined the delayed effects of PM_2.5 concentrations for the short-lag (1-4 weeks prior) and the long-lag (during the prior wildfire season months) on the following winter influenza season in Montana, a mountainous state in the western United States. We created gridded maps of surface PM_2.5 for the state of Montana from 2009 to 2018 using spatial regression models fit with station observations and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness data. We used a seasonal quasi-Poisson model with generalized estimating equations to estimate weekly, county-specific, influenza counts for Montana, associated with delayed PM_2.5 concentration periods (short-lag and long-lag effects), adjusted for temperature and seasonal trend. We did not detect an acute, short-lag PM_2.5 effect nor short-lag temperature effect on influenza in Montana. Higher daily average PM_2.5 concentrations during the wildfire season was positively associated with increased influenza in the following winter influenza season (expected 16% or 22% increase in influenza rate per 1 μg/m³ increase in average daily summer PM_2.5 based on two analyses, p = 0.04 or 0.008). This is one of the first observations of a relationship between PM_2.5 during wildfire season and influenza months later.

The most destructive wildland fires occur in mixtures of living and dead vegetation, yet very little attention has been given to the fundamental differences between factors that control their flammability. Historically, moisture content has been used to evaluate the relative flammability of live and dead fuels without considering major, unreported differences in the factors that control their variations across seasons and years. Physiological changes at both the leaf and whole plant level have the potential to explain ignition and fire behavior phenomena in live fuels that have been poorly explained for decades. Here, we explore how these physiological changes violate long-held assumptions about live fuel dynamics and we present a conceptual model that describes how plant carbon and water cycles independently and interactively influence plant flammability characteristics at both the leaf and whole plant scale. This new ecophysiology-based approach can help us expand our understanding of potential plant responses to environmental change and how those physiological changes may impact plant flammability. Furthermore, it may ultimately help us better manage wildland fires in an uncertain future.

Despite major advances in numerical weather prediction, few resources exist to forecast wildland fire danger conditions to support operational fire management decisions and community early-warning systems. Here we present the development and evaluation of a spatial fire danger index that can be used to assess historical events, forecast extreme fire danger, and communicate those conditions to both firefighters and the public. It uses two United States National Fire Danger Rating System indices that are related to fire intensity and spread potential. These indices are normalized, combined, and categorized based on a 39-yr climatology (1979–2017) to produce a single, categorical metric called the Severe Fire Danger Index (SFDI) that has five classes; Low, Moderate, High, Very High, and Severe. We evaluate the SFDI against the number of newly reported wildfires and total area burned from agency fire reports (1992–2017) as well as daily remotely sensed numbers of active fire pixels and total daily fire radiative power for large fires (2003–2016) from the Moderate-Resolution Imaging Spectroradiometer (MODIS) across the conterminous United States. We show that the SFDI adequately captures geographic and seasonal variations of fire activity and intensity, where 58% of the eventual area burned reported by agency fire records, 75.2% of all MODIS active large fire pixels, and 81.2% of all fire radiative power occurred when the SFDI was either Very High or Severe (above the 90th percentile). We further show that SFDI is a strong predictor of firefighter fatalities, where 97 of 129 (75.2%) burnover deaths from 1979 to 2017 occurred when SFDI was either Very High or Severe. Finally, we present an operational system that uses short-term, numerical weather predictions to produce daily SFDI forecasts and show that 76.2% of all satellite active fire detections during the first 48 h following the ignition of nine high-profile case study fires in 2017 and 2018 occurred under Very High or Severe SFDI conditions. The case studies indicate that the extreme weather events that caused tremendous damage and loss of life could be mapped ahead of time, which would allow both wildland fire managers and vulnerable communities additional time to prepare for potentially dangerous conditions. Ultimately, this simple metric can provide critical decision support information to wildland firefighters and fire-prone communities and could form the basis of an early-warning system that can improve situational awareness and potentially save lives.