Fire Weather Index Research Articles

Assessment of lake-level fluctuation as an indicator of fire activity in boreal Canada

Fuel moisture content is a highly variable property that dictates the ignition and combustion potentials of wildfire fuels. The Canadian Fire Weather Index (FWI) System—a subset of the established Canadian Forest Fire Danger Rating System—relies solely on weather observations to track fuel moisture and compute indices of fire potential. Ancillary environmental measures, such as lake levels in non-regulated lake systems, may integrate climatic and landscape factors that better reflect environmental moisture deficits and thereby complement the FWI System. Accordingly, we investigated whether inter-annual variation in lake levels has potential to serve as an indicator of regional fire activity using a quasi-binomial regression approach. This study combines annual measurements of fire activity, lake level, and other climatic variables from the Boreal Shield and Boreal Plain ecozones of Canada from 1980 to 2019 to determine whether variation in regional area burned can be explained by lake-level fluctuation, and whether this explanatory power differs by ecozone. Results indicated that lake-level fluctuation is a significant predictor of regional area burned for many lakes (28–48%). It often performs as well as or better than traditional fire-weather metrics (such as temperature and precipitation), and this relationship is stronger on the Boreal Shield compared to the Boreal Plain. Lake-level fluctuation provides information on fuel availability unaccounted for by traditional fire-weather metrics, suggesting that this variable could be added to the suite of environmental indicators of regional fire activity in Canada.

Summer and Fall Extreme Fire Weather Projected to Occur More Often and Affect a Growing Portion of California throughout the 21st Century

Annual burned area has increased in California over the past three decades as a result of rising temperatures and a greater atmospheric demand for moisture, a trend that is projected to continue throughout the 21st century as a result of climate change. Here, we implement a bias-correction and statistical downscaling technique to obtain high resolution, daily meteorological conditions for input into two fire weather indices: vapor pressure deficit (VPD) and the Canadian Fire Weather Index System (FWI). We focus our analysis on 10 ecoregions that together account for the diverse range of climates, ecosystems, topographies, and vegetation types found across the state of California. Our results provide evidence that fire weather conditions will become more extreme and extend into the spring and fall seasons in most areas of California by 2100, extending the amount of time vegetation is exposed to increased atmospheric demand for moisture, and heightening the overall risk for the ignition and spread of large wildfire. The ecoregion-level spatial scale adopted for this study increases the spatial specificity of fire weather information, as well as the resolution with which fire and land managers can implement strategies and counter-measures when addressing issues related to climate change.

The 2017 Split wildfire in Croatia: evolution and the role of meteorological conditions

Abstract. The Split wildfire in July 2017, which was one of the most severe wildfires in the history of this Croatian World Heritage Site, is the focus in this study. The Split fire is a good example of a wildfire–urban interface, with unexpected fire behavior including rapid downslope spread to the coastal populated area. This study clarifies the meteorological conditions behind the fire event, those that have limited the effectiveness of firefighting operations, and the rapid escalation and expansion of the fire zones within 30 h. The Split fire propagation was first reconstructed using radio logs, interviews with firefighters and pilots involved in the intervention, eyewitness statements, digital photographs from fire detection cameras, media, and the monthly firefighting journal. Four phases of fire development have been identified. Then, weather observations and numerical simulations using an enhanced-resolution operational model are utilized to analyze the dynamics in each phase of the fire runs. The synoptic background of the event includes large surface pressure gradient between the Azores anticyclone accompanied by a cold front and a cyclone over the southeastern Balkan Peninsula. At the upper level, there was a deep shortwave trough extending from the Baltic Sea to the Adriatic Sea, which developed into a cut-off low. Such synoptic conditions have resulted in the maximum fire weather index in 2017. Combined with topography, they also locally provoke the formation of the strong northeasterly bura wind along the Adriatic coast, which has been accompanied by a low-level jet (LLJ). The bura (downslope wind), with mid- to low-level gravity-wave breaking and turbulence mixing (as in the hydraulic jump theory), also facilitated the subsidence of dry air from the upper troposphere and rapid drying at the surface. This study demonstrates that numerical guidance that indicates the spatial and temporal occurrence of a LLJ is highly capable of explaining the Split fire evolution from the ignition potential to its extinguishment stage. Thus, in addition to the conventional fire weather indices, such products are able to improve fire weather behavior forecasting and in general more effective decision-making in fire management.

Contribution to the Study of Forest Fires in Semi-Arid Regions with the Use of Canadian Fire Weather Index Application in Greece

Forest fires are of critical importance in the Mediterranean region. Fire weather indices are meteorological indices that produce information about the impact as well as the characteristics of a fire event in an ecosystem and have been developed for that reason. This study explores the spatiotemporal patterns of the FWI system within a study area defined by the boundaries of the Greek state. The FWI has been calculated and studied for current and future periods using data from the CFSR reanalysis model from the National Centers for Environmental Protection (NCEP) as well as data from NASA satellite programs and the European Commission for Medium-Range Weather Forecasts (ECWMF) in the form of netCDF files. The calculation and processing of the results were conducted in the Python programming language, and additional drought- and fire-related indices were calculated, such as the standardized precipitation index (SPI), number of consecutive 50-day dry periods (Dry50), the Fosberg fire weather index (FFWI), the days where the FWI exceeds values of 40 and 50 days (FWI > 40) and (days FWI > 50). Similar patterns can easily be noted for all indices that seem to have their higher values concentrated in the southeast of the country owing to the higher temperatures and more frequent drought events that affect the indices’ behavior in both the current and future periods.

Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe

The impacts and threats posed by wildfires are dramatically increasing due to climate change. In recent years, the wildfire community has attempted to estimate wildfire occurrence with machine learning models. However, to fully exploit the potential of these models, it is of paramount importance to make their predictions interpretable and intelligible. This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation. Our findings accurately detected regions with a high presence of wildfires (area under the curve 81.3%) and outlined the drivers empowering occurrence, such as the Fire Weather Index and Normalized Difference Vegetation Index. Furthermore, our analysis suggests the presence of anomalous hotspots. In contexts where human and natural spheres constantly intermingle and interact, the XAI framework, suitably integrated into decision support systems, could support forest managers to prevent and mitigate future wildfire disasters and develop strategies for effective fire management, response, recovery, and resilience.

Semantic sensor network ontology based decision support system for forest fire management

The forests are significant assets for every country. When it gets destroyed, it may negatively impact the environment, and forest fire is one of the primary causes. Fire weather indices are widely used to measure fire danger and issue bushfire warnings. It can also be used to predict the demand for emergency management resources. Sensor networks have grown in popularity in data collection and processing capabilities for various applications in industries such as medical, environmental monitoring, home automation, etc. Semantic sensor networks can collect various climatic circumstances like wind speed, temperature, and relative humidity. However, estimating fire weather indices is challenging due to the various issues involved in processing the data streams generated by the sensors. Hence, the importance of forest fire detection has increased day by day. The underlying Semantic Sensor Network (SSN) ontologies are built to allow developers to create rules for calculating fire weather indices and the converted dataset into Resource Description Framework (RDF). This research describes the various steps in developing rules for calculating fire weather indices. Besides, this work presents a Web-based mapping interface to help users visualize the changes in fire weather indices over time. With the help of the inference rule, it designed a decision support system using the SSN ontology and query on it through SPARQL. The proposed fire management system acts according to the situation and supports reasoning and the general semantics of the open world, followed by all the ontologies.

On the selection of an interpolation method with an application to the Fire Weather Index in Ontario, Canada

AbstractEvidence‐based studies in the environmental sciences frequently rely on the presence of spatially dense climatological data. However, such data are often available only at a fixed set of locations that may be regularly or irregularly arranged across a region. Spatial interpolation enables the approximation of variables of interest at locations between those sites. When conducting interpolation in collaboration with an end user or in interdisciplinary research, mutual knowledge exchange allows for greater insight on what is required of an interpolation method since each may have different pros and cons. We outline and discuss several key considerations one should make in an interpolation study, such as the purpose of the variable and the goals of the end user, including how the variable is used to inform decisions. This process is then illustrated via case study within a wildland fire weather context. For the province of Ontario, Canada, we contrast several methods for interpolating the Fire Weather Index (FWI), comparing them quantitatively via metrics and qualitatively using a proposed categorical gradients visualization scheme. Conditional simulations and a spatial ensemble are also investigated. This work is in collaboration with the Ontario Ministry of Natural Resources and Forestry.

Wildfire Danger Prediction and Understanding With Deep Learning

AbstractClimate change exacerbates the occurence of extreme droughts and heatwaves, increasing the frequency and intensity of large wildfires across the globe. Forecasting wildfire danger and uncovering the drivers behind fire events become central for understanding relevant climate‐land surface feedback and aiding wildfire management. In this work, we leverage Deep Learning (DL) to predict the next day's wildfire danger in a fire‐prone part of the Eastern Mediterranean and explainable Artificial Intelligence (xAI) to diagnose model attributions. We implement DL models that capture the temporal and spatio‐temporal context, generalize well for extreme wildfires, and demonstrate improved performance over the traditional Fire Weather Index. Leveraging xAI, we identify the substantial contribution of wetness‐related variables and unveil the temporal focus of the models. The variability of the contribution of the input variables across wildfire events hints into different wildfire mechanisms. The presented methodology paves the way to more robust, accurate, and trustworthy data‐driven anticipation of wildfires.

The Performance of ECMWF Ensemble Prediction System for European Extreme Fires: Portugal/Monchique in 2018

At the beginning of August 2018, Portugal experienced a severe heat episode over a few days that consequently increased the probability of wildfire events. Due to the advection of an anomalous very hot and dry air mass, severe fire-prone meteorological conditions were forecasted mainly over southern Portugal, in the Monchique region. Together with the significant fuel amount accumulated since the last extreme wildfire in August 2003, all the unfavorable conditions were set to drive a severe fire over this region. The Monchique fire started on 3 August 2018, being very hard to suppress and lasting for seven days, with a burnt area of 27,000 ha. Regarding the need to have operational early warning tools, this work aims to evaluate the reliability of fire probabilistic products, up to 72 h ahead, together with the use of fire radiative power products, as support tools in fire monitoring and resource activities. To accomplish this goal, we used the fire probabilistic products of the Ensemble Prediction System, provided by the Copernicus Atmosphere Monitoring Service. Among available fire danger rating systems, the Fire Weather Index and the Fine Fuels Moisture Code of the Canadian Forest Fire Weather Index System were selected to assess the meteorological fire danger. The assessment of the fire intensity was based on the Fire Radiative Energy released, considering the Fire Radiative Power, delivered in near real-time, by EUMETSAT Land Surface Analysis Satellite Applications Facility. The exceptional fire danger over southern Portugal that favors the ignition of the Monchique fire and its severity was essential driven by two important factors: (i) the anomalous fire weather danger, before and during the event; (ii) the accumulated fuel amount, since the last severe event occurred in 2003, over the region. Results show that the selected fire probabilistic products described the meteorological fire danger observed well, and the LSA-SAF products revealed the huge amount of fire energy emitted, in line with the difficulties faced by authorities to suppress the Monchique fire.

Mesoscale patterns associated with two distinct heatwave events in coastal Santa Barbara, California, and their impact on local fire risk conditions

This study investigates mesoscale mechanisms associated with two extreme heatwaves affecting Santa Barbara County (SBC), southern California, and their implication for severe fire weather. We examine these issues using a database consisting of surface stations, radiosoundings, and 1 km grid spacing simulations with the Weather Research & Forecasting (WRF), including a climatology spanning 32 years. During the first heatwave event, synoptic conditions induced downslope winds on the southern-facing slopes of the Santa Ynez Mountains (SYM) on July 6, 2018. One surface station hit an all-time record, and nine surface stations exceeded 99.9th percentiles of surface temperatures. A wildfire (the Holiday Fire) erupted on the slopes of the SYM driven by high temperatures, low relative humidity, and strong winds. The nearby radiosonde registered temperatures at 850 hPa that exceeded the 95th percentile historical records (62 yrs). WRF simulations indicated that mountain wave activity contributed to the excessive surface temperatures on the south-facing slopes of the SYM, and explained the late evening timing for the maximum daily temperatures. The second heatwave broke all-time temperature records at 10 surface stations across SBC on September 6, 2020. Maximum temperatures for most of the SBC occurred during mid-afternoon, the highest observed temperature at the surface was 48.3 °C (118.9∘F), and the 850 hPa temperatures exceeded the 99th percentile. The September 2020 event occurred under weaker synoptic forcing (pressure gradients) than the July 2018 event, resulting in weaker winds in coastal Santa Barbara (including the slopes of the SYM) and Santa Ynez Valley. Nonetheless, the extreme heat and low relative humidity increased the Fosberg Fire Weather Index (FFWI) at critical values for a few hours in some sites when winds were moderate. To evaluate the relative importance of these extreme events in the historical context and to assess the region’s wildfire risk we propose a novel diagram based on the joint behavior of winds, temperature, humidity and FFWI. While no wildfires have broken out during the September 2020 heatwave, our analysis suggests that a combination of extreme heat with stronger winds would lead to unprecedented fire danger. These extreme conditions may become more common in a warming planet.

Dynamics of fire weather conditions in the mixed forest areas of Belarus and Ukraine under recent climate change

The study examines spatiotemporal distribution of fire weather conditions during the fire seasons of 1990–2020 in the mixed forests areas of the territory of Belarus and Ukraine using monthly mean Fire Weather Index (FWI) averaged for each administrative area in the study region. It was revealed that the lowest FWI values were observed in the northern and northwestern regions of Belarus, the highest values were in the southeast. On the territory of Ukraine, FWI values increase toward the east regions. In the seasonal distribution, the FWI increases from March to May and in the middle of summer the index reaches a maximum in the all regions. Analysis of the FWI dynamics over the five-year periods showed that the frequency of danger fire weather conditions varied from a "very low" level to "moderate" level most of the study period. In the last pentad 2015–2020, "high" fire danger level began to appear in the south of Belarus and in the northern part of Ukraine. During the study period, in some regions of Ukraine, there was a decrease in the frequency of the FWI values of 'low' danger level and its simultaneous increase at the higher levels, in connection with the observed climate change in the region.

Global Wildfire Susceptibility Mapping Based on Machine Learning Models

Wildfires are a major natural hazard that lead to deforestation, carbon emissions, and loss of human and animal lives every year. Effective predictions of wildfire occurrence and burned areas are essential to forest management and firefighting. In this paper we apply various machine learning (ML) methods on a 0.25° monthly resolution global dataset of wildfires. We test the prediction accuracies of four different fire occurrence classifiers: random forest (RF), eXtreme Gradient Boosting (XGBoost), multilayer perceptron (MLP) neural network, and a logistic regression. Our best ML model predicts wildfire occurrence with over 90% accuracy, compared to approximately 70% using a logistic regression. We then train ML regression models to predict the size of burned areas and obtain an MAE score of 3.13 km2, compared to 7.48 km2 using a linear regression. To the best of our knowledge, this is the first study to be conducted in such resolution on a global dataset. We use the developed models to shed light on the influence of various factors on wildfire occurrence and burned areas. We suggest building upon these results to create ML-based fire weather indices.

Projections of wildfire weather danger in the Canary Islands

Climate change is expected to enhance weather conditions prone to wildfires. Climate regionalized projections for the Canary Islands were performed, using as boundary conditions some of the results provided by the Coupled Model Intercomparison Project (CMIP5) initiative, and covering the recent past (1980–2009) and future (2070–2099) periods, under two Representative Concentration Pathways, 4.5 and 8.5. All fire risk indicators derived from the Canadian Forest Fire Weather Index (FWI) are foreseen to worsen by the end of the century. The fire season could increase its length up to 75 days per year, being more noticeable as altitude increases. The extreme risk days (FWI > 60) show an average increase of 58%, reaching 12 days a year, and the area with high risk could increase by 44%. Analyzing the contribution of the different meteorological variables, it is observed that the main parameter in the fire danger index result is the temperature (currently weights 46%). However, in the future, the importance of precipitation will increase, since the rainfall reduction in some areas could reach 41%. The high dependence of the expected changes on land height, and the small size of the islands, demonstrates the necessity of using high-resolution climate regionalizations.

Drivers of extreme wildfire years in the 1965–2019 fire regime of the Tłı̨chǫ First Nation territory, Canada

ABSTRACT Exceptionally large areas burned in 2014 in central Northwest Territories (Canada), leading members of the Tłı̨chǫ First Nation to characterize this year as ‘extreme’. Top-down climatic and bottom-up environmental drivers of fire behavior and areas burned in the boreal forest are relatively well understood, but not the drivers of extreme wildfire years (EWY). We investigated the temporal and spatial distributions of fire regime components (fire occurrence, size, cause, fire season length) on the Tłı̨chǫ territory from 1965 to 2019. We used BioSIM and data from weather stations to interpolate mean weather conditions, fuel moisture content and fire-weather indices for each fire season, and we described the environmental characteristics of burned areas. We identified and characterized EWY, i.e., years exceeding the 80th percentile of annual area burned for the study period. Temperature and fuel moisture were the main drivers of areas burned. Nine EWY occurred from 1965 to 2019, including 2014. Compared to non-EWY, EWY had significantly higher mean temperature (>14.7°C) and exceeded threshold values of Drought Code (>514), Initial Spread Index (>7), and Fire Weather Index (>19). Our results will help limit the effects of EWY on human safety, health and Indigenous livelihoods and lifestyles.

Climate drivers of global wildfire burned area

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.

Future climate change impact on wildfire danger over the Mediterranean: the case of Greece

Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.

Performances of climatic indicators from seasonal forecasts for ecosystem management: The case of Central Europe and the Mediterranean

Climate change is affecting the interannual variability and the seasonal cycle for key meteorological variables, as well as the frequency, intensity, duration and timing of out-of-the-norm and extreme events. In such a context, seasonal forecasts (i.e., climate predictions from a few weeks to several months ahead) are compelling instruments able to anticipate upcoming climate risks and guide tactical decisions. In this study, we assess the performance of seasonal forecasts for the summer period across Central Europe and the Mediterranean region in predicting some key indicators serving agriculture and forestry sectors, i.e., Potential EvapoTranspiration (PET), Potential Soil Moisture Deficit (PSMD), and Fire Weather Index (FWI). We exploit authoritative climate data from Copernicus Climate Data Store, i.e., ERA5 reanalyses and hindcasts from CMCC SPSv3 and ECMWF SEAS5 seasonal prediction systems (SPSs), to evaluate, using both deterministic and probabilistic evaluation scores, differences in performance across the two SPSs, two start dates (March and May) and four correction techniques applied to overcome modelling bias, namely bias correction (BC), calibration (CAL), quantile mapping (QM) and detrending (DET). Results show that seasonal predictions of PET perform better in Western and Eastern Europe and some areas of North Africa. PSMD predictions follow a similar spatial pattern as PET, except that for some areas in Central (Eastern) Europe in which the performance increases (decreases). FWI predictions reveal better results in some areas of the Iberian Peninsula, North-Western Africa, Balkan Peninsula, and Ukraine. Results also suggest that QM might be the most suitable technique for bias correction. Furthermore, the start date of the forecast might imply varying correlation significance, with the start date closest to the forecasted period not always being the best. Overall, our study suggests the potential usefulness of seasonal forecasts for decision making under different geographical and environmental contexts, considering sensitivity inherent to different processing chains.

Resilient by design: Preventing wildfires and blackouts with microgrids

This paper proposes a strategy for managing wildfire risks and preventing blackouts using microgrids. To demonstrate this approach, not seen in previous literature, we use the power network of Victoria, Australia, in December 2019 as a case study. The Fire Weather Index (FWI) is a crucial indicator of global fire behaviour both spatially and temporally, as proved with its robust analysis within many previous studies. The FWI is applied to a Wildfire-Energy System for the first time, contributing to a higher spatial and temporal resolution to position the wildfire risk in a grid. A novel method is proposed to automatically correlate the wildfire risk index and the power network model using geographical information of the transmission lines. The optimal power flow and grid performances are obtained from a grid model which incorporates wildfire risk distributions. It is shown that a system with installed microgrids can maintain operation under severe fire-related conditions without scheduled or unplanned outages. Finally, a cost-benefit analysis is conducted, which demonstrates that 68% of system costs can be recuperated by implementing networked microgrid solutions.

Spatiotemporal Dynamics and Climate Influence of Forest Fires in Fujian Province, China

Climate determines the spatiotemporal distribution pattern of forest fires by affecting vegetation and the extent of drought. Thus, analyzing the dynamic change of the forest fire season and its response to climate change will play an important role in targeted adjustments of forest fire management policies and practices. In this study, we studied the spatiotemporal variations in forest fire occurrence in Fujian Province, China using the Mann–Kendall trend test and correlation analysis to analyze Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 and meteorological data. The results show that forest fire occurrence rose first and then declined over the years, but the proportion of forest fires during the fire prevention period decreased. The forest fires increased significantly in spring and summer, exceeding the forest fires occurring in the fire prevention period in 2010. The spatial distribution of forest fires decreased from northwest to southeast coastal areas, among which the number of forest fires in the northwest mountainous areas was large in autumn and winter. The fire risk weather index was strongly and positively correlated with forest fire occurrence across various sites in the province. The findings accentuate the need for properly adjusting the fire prevention period and resource allocation, strengthening the monitoring and early warning of high fire risk weather, and publicizing wildfire safety in spring and summer. As the forest fire occurrence frequency is high in the western and northwest mountainous areas, more observation towers and forest fire monitoring facilities should be installed.

Past and future trends in fire weather for the UK

Abstract. Past and future trends in the frequency of high-danger fire weather conditions have been analysed for the UK. An analysis of satellite-derived burned area data from the last 18 years has identified the seasonal cycle with a peak in spring and a secondary peak in summer, a high level of interannual variability, and a lack of a significant trend despite some large events occurring in the last few years. These results were confirmed with a longer series of fire weather indices back to 1979. The Initial Spread Index (ISI) has been used for spring, as this reflects the moisture of fine fuel surface vegetation, whereas conditions conducive to summer wildfires are hot, dry weather reflected in the moisture of deeper organic layers which is encompassed in the Fire Weather Index (FWI). Future projections are assessed using an ensemble of regional climate models from the UK Climate Projections, combining variables to derive the fire weather indices. The results show a large increase in hazardous fire weather conditions in summer. At 2 ∘C global warming relative to 1850–1900, the frequency of days with “very high” fire danger is projected to double compared to the recent historical period. This frequency increases by a factor of 5 at 4 ∘C of global warming. Smaller increases are projected for spring, with a 150 % increase for England at 2 ∘C of global warming and a doubling at 4 ∘C. A particularly large projected increase for late summer and early autumn suggests a possible extension of the wildfire season, depending on fuel availability. These results suggest that wildfire can be considered an “emergent risk” for the UK, as past events have not had widespread major impacts, but this could change in future, with adaptation actions being required to manage the future risk. The large increase in risk between the 2 and 4 ∘C levels of global warming highlights the importance of global efforts to keep warming below 2 ∘C.