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Abstract. Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai’i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society’s resilience to wildfires and promote advances in preparedness, mitigation, and adaptation.

Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km 2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from 10.5281/zenodo.11400539 (Jones et al., 2024) and 10.5281/zenodo.11420742 (Kelley et al., 2024a). [ABSTRACT FROM AUTHOR]

Peat fires in the northern high latitudes have the potential to burn vast amounts of carbon-rich organic soil, releasing large quantities of long-term stored carbon to the atmosphere. Due to anthropogenic activities and climate change, peat fires are increasing in frequency and intensity across the high latitudes. However, at present they are not explicitly included in most fire models. Here we detail the development of INFERNO-peat, the first parameterization of peat fires in the JULES-INFERNO (Joint UK Land Environment Simulator INteractive Fire and Emission algoRithm for Natural envirOnments) fire model. INFERNO-peat utilizes knowledge from lab and field-based studies on peat fire ignition and spread to be able to model peat burnt area, burn depth, and carbon emissions, based on data of the moisture content, inorganic content, bulk density, soil temperature, and water table depth of peat. INFERNO-peat improves the representation of burnt area in the high latitudes, with peat fires simulating on average an additional 0.305×106 km 2 of burn area each year, emitting 224.10 Tg of carbon. Compared to Global Fire Emissions Database version 5 (GFED5), INFERNO-peat captures ∼ 20 % more burnt area, whereas INFERNO underestimated burning by 50 %. Additionally, INFERNO-peat substantially improves the representation of interannual variability in burnt area and subsequent carbon emissions across the high latitudes. The coefficient of variation in carbon emissions is increased from 0.071 in INFERNO to 0.127 in INFERNO-peat, an almost 80 % increase. Therefore, explicitly modelling peat fires shows a substantial improvement in the fire modelling capabilities of JULES-INFERNO, highlighting the importance of representing peatland systems in fire models. [ABSTRACT FROM AUTHOR]

Peat fires in the Northern high latitudes have the potential to burn vast amounts of carbon rich organic soil, releasing large quantities of long-term stored carbon to the atmosphere. Due to anthropogenic activities and climate change, peat fires are increasing in frequency and intensity across the high latitudes. However, at present they are not explicitly included in most fire models. Here we detail the development of INFERNO-peat, the first parameterisation of peat fires in the JULES-INFERNO fire model. INFERNO-peat utilises knowledge from lab and field-based studies on peat fire ignition and spread to be able to model peat burnt area, burn depth and carbon emissions, based on data of the moisture content, inorganic content, bulk density, soil temperature and water table depth of peat. INFERNO-peat improves the representation of burnt area in the high latitudes, with peat fires simulating on average an additional 0.305 M km2 of burn area each year, emitting 224.10 Tg of carbon. Compared to GFED5, INFERNO-peat captures ~20 % more burnt area, whereas INFERNO underestimated burning by 50 %. Additionally, INFERNO-peat substantially improves the representation of interannual variability in burnt area and subsequent carbon emissions across the high latitudes. The coefficient of variation in carbon emissions is increased from 0.071 in INFERNO to 0.127 in INFERNO-peat, an almost 80 % increase. Therefore, explicitly modelling peat fires shows a substantial improvement in the fire modelling capabilities of JULES-INFERNO, highlighting the importance of representing peatland systems in fire models. [ABSTRACT FROM AUTHOR]

Humans can act as fire starters or suppressors, changing fire regimes by increasing the number of ignitions, changing their timing, and altering fuel structure and abundance, which can be considered a human--environmental coupling. Considering the human influences on fire activity, representing socio-economic impacts on fires in global fire models is crucial to underpin the confidence in these modelling frameworks. In this work we implement a socio-economic factor in the fire ignition and suppression parametrisation in INFERNO based on a Human Development Index (HDI). HDI captures human development's income, health, and education dimensions leading to a representation where if there is more effort to improve human development, the population also invests in higher fire suppression. Including this representation of socio-economic factors in INFERNO reduces the annual mean burnt area (between 1997 - 2016) positive biases found in Temperate North America, Central America, Europe and Southern Hemisphere South America, by more than 100 % without statistically significant impact to other areas. In addition, it improves the representation of the burnt area trends, especially in Africa. Central Asia and Australia where observations show negative trends. Including socio-economic impacts on fire based on HDI in INFERNO provides a simple and linear representation of these effects on fire ignition and suppression, leading to an improvement of the model performance, especially in developed regions, These impacts are especially relevant to understand future climate regimes and inform policymakers on effects of fire policy in a changing climate. [ABSTRACT FROM AUTHOR]

Land management and deforestation in tropical regions cause wildfires and forest degradation, leading to a loss of ecosystem services and global climate regulation. The objective of the study was to provide a comprehensive assessment of the spatial extent and patterns of burned areas in a new deforestation frontier in the Amazonas state. The methodology applied cross-referenced burned area data from 2003 to 2019 with climate, land cover, private properties and Protected Areas information and performed a series of statistical tests. The influence of the Multivariate ENSO Index (MEI) contributed to a decreasing rainfall anomalies trend and increasing temperature anomalies trend. This process intensified the dry season and increased the extent of annual natural vegetation affected by fires, reaching a peak of 681 km2 in 2019. The results showed that the increased deforestation trend occurred mostly in public lands, mainly after the new forest code, leading to an increase in fires from 66 to 84% in 2019. The methods developed here could identify fire extent, trends, and relationship with land cover change and climate, thus pointing to priority areas for preservation. The conclusion presented that policy decisions affecting the Amazon Forest must include estimates of fire risk and impact under current and projected future climates. [ABSTRACT FROM AUTHOR]

Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by the atmospheric models' climate biases, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES. [ABSTRACT FROM AUTHOR]