# State of Wildfires 2023–2024

Matthew Jones, Douglas Kelley, Chantelle Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, Esther Brambleby, Andrew Hartley, Anna Lombardi, Guilherme Mataveli, Joe Mcnorton, et al.

► To cite this version:

Matthew Jones, Douglas Kelley, Chantelle Burton, Francesca Di Giuseppe, Maria Lucia F. Barbosa, et al. State of Wildfires 2023–2024. Earth System Science Data, 2024, 16 (8), pp.3601-3685. 10.5194/essd-16-3601-2024 . hal-04676059

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State of Wildfires 2023–2024

Matthew W. Jones<fnref n="1" />,*, Douglas I. Kelley<fnref n="2" />,*, Chantelle A. Burton<fnref n="3" />,*, Francesca Di Giuseppe<fnref n="4" />,*,

Maria Lucia F. Barbosa<fnref n="5" />,<fnref n="6" />, Esther Brambleby<fnref n="1" />, Andrew J. Hartley<fnref n="3" />, Anna Lombardi<fnref n="7" />,

Guilherme Mataveli<fnref n="8" />,<fnref n="1" />, Joe R. McNorton<fnref n="4" />, Fiona R. Spuler<fnref n="9" />, Jakob B. Wessel<fnref n="10" />,<fnref n="11" />, John T. Abatzoglou<fnref n="12" />,

Liana O. Anderson<fnref n="13" />, Niels Andela<fnref n="14" />, Sally Archibald<fnref n="15" />, Dolors Armenteras<fnref n="16" />, Eleanor Burke<fnref n="3" />,

Rachel Carmenta<fnref n="17" />, Emilio Chuvieco<fnref n="18" />, Hamish Clarke<fnref n="19" />, Stefan H. Doerr<fnref n="20" />, Paulo M. Fernandes²¹,

Louis Giglio²², Douglas S. Hamilton²³, Stijn Hantson²⁴, Sarah Harris²⁵, Piyush Jain²⁶,

Crystal A. Kolden²⁷, Tiina Kurvits²⁸, Seppe Lampe²⁹, Sarah Meier³⁰, Stacey New<fnref n="3" />, Mark Parrington³¹,

Morgane M. G. Perron³², Yuquan Qu³³,³⁴, Natasha S. Ribeiro³⁵, Bambang H. Saharjo³⁶,

Jesus San-Miguel-Ayanz³⁷, Jacquelyn K. Shuman³⁸, Veerachai Tanpipat³⁹, Guido R. van der Werf⁴⁰,

Sander Veraverbeke³³,<fnref n="1" />, and Gavriil Xanthopoulos⁴¹

<footnote n="1">¹Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia,
Norwich Research Park, Norwich, NR4 7TJ, UK</footnote>

<footnote n="2">²Hydro-climate risks, UK Centre for Ecology and Hydrology, Wallingford, OX10 8BB, UK</footnote>

<footnote n="3">³Hadley Centre, Met Office, Fitzroy Road, Exeter, EX1 3PB, UK</footnote>

<footnote n="4">⁴Earth System Modelling Section, Forecast Department, European Centre for Medium-range Weather
Forecasts, Shinfield Park, Reading, RG2 9AX, UK</footnote>

<footnote n="5">⁵Department of Remote Sensing, National Institute for Space Research, Avenida dos Astronautas, 1758 - Jd,
Granja - São José dos Campos - São Paulo, 12227-010, Brazil</footnote>

<footnote n="6">⁶Natural Sciences Center, Federal University of São Carlos, Rodovia Lauri Simões de Barros,
mk 12 - SP-189 - Aracaçu, Buri - São Paulo, 18290-000, Brazil</footnote>

<footnote n="7">⁷Climate Intelligence, Research Department, European Centre for Medium-range Weather Forecasts,
Shinfield Road, Reading, RG2 9AX, UK</footnote>

<footnote n="8">⁸Earth Observation and Geoinformatics Division, National Institute for Space Research, Avenida dos
Astronautas, 1758. Jd Granja – São José dos Campos – São Paulo, 12227-010, Brazil</footnote>

<footnote n="9">⁹Department of Meteorology, University of Reading, Earley Gate,
Whiteknights Rd, Reading, RG6 6ET, UK</footnote>

<footnote n="10">¹⁰Department of Mathematics and Statistics, Harrison Building, University of Exeter,
North Park Road, Exeter, UK</footnote>

<footnote n="11">¹¹The Alan Turing Institute, British Library, 96 Euston Road, London, UK</footnote>

<footnote n="12">¹²School of Engineering, University of California, Merced, 5200 N Lake Rd, Merced, CA 95343, USA</footnote>

<footnote n="13">¹³Cemaden/MCTI, Estrada Doutor Altino Bondensan, 500 - Distrito de Eugênio de Melo,
São José dos Campos - São Paulo, Brazil</footnote>

<footnote n="14">¹⁴BeZero Carbon, 25 Christopher Street, London, EC2A 2BS, UK</footnote>

<footnote n="15">¹⁵School of Animal Plant and Environmental Sciences, University of the Witwatersrand Johannesburg,
University Corner, Braamfontein, Johannesburg, South Africa</footnote>

<footnote n="16">¹⁶Landscape Ecology and Ecosystem Modelling Group, Faculty of Sciences, Department of Biology,
Universidad Nacional de Colombia, Cra. 30 no. 45-03, Bogotá D.C., CP 111321, Colombia</footnote>

<footnote n="17">¹⁷Tyndall Centre for Climate Change Research, School of Global Development, University of East Anglia,
Norwich Research Park, Norwich, NR4 7TJ, UK</footnote>

<footnote n="18">¹⁸Department of Geology, Geography and the Environment, Universidad de Alcalá,
Colegios, 2 - 28801 Alcalá de Henares, Spain</footnote>

<footnote n="19">¹⁹FLARE Wildfire Research, School of Agriculture, Food and Ecosystem Sciences,
University of Melbourne, Grattan St, Parkville, 3010, Australia</footnote>

<footnote n="20">²⁰Centre for Wildfire Research, Swansea University, Singleton Park, Swansea, SA2 8PP, Wales, UK</footnote>
<sup>21</sup>ForestWISE – Collaborative Laboratory for Integrated Forest and Fire Management, Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto
Douro, Quinta de Prados, Vila Real, 5000-801, Portugal

<sup>22</sup>Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

<sup>23</sup>Marine, Earth and Atmospheric Science, North Carolina State University, Raleigh,
NC 27695, USA

<sup>24</sup>Program in Earth System Sciences, Faculty of Natural Sciences, Universidad del Rosario, Bogotá, Colombia

<sup>25</sup>Fire Risk, Research and Community Preparedness, Country Fire Authority, Burwood East, Victoria, Australia

<sup>26</sup>Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, 5320 122 St NW,
Edmonton, AB T6H 3S5, Canada

<sup>27</sup>Wildfire Resilience Center, School of Engineering, University of California, Merced, 5200 N Lake Rd,
Merced, CA 95343, USA

<sup>28</sup>GRID-Arendal, P.O. Box 183, 4802, Arendal, Norway

<sup>29</sup>Department of Water and Climate, Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

<sup>30</sup>Land, Environment, Economics and Policy Institute, Department of Economics, University of Exeter,
Rennes Drive, Exeter, EX4 4ST, UK

<sup>31</sup>Atmospheric Composition Section, Research Department, European Centre for Medium-range Weather
Forecasts, Robert-Schuman-Platz 3, 53175 Bonn, Germany

<sup>32</sup>UMR 6539 CNRS/IRD/Ifremer/LEMAR, Institut Universitaire Européen de la Mer,
University of Brest, 29280 Plouzané, France

<sup>33</sup>Department of Earth Sciences, Faculty of Science, Vrije Universiteit Amsterdam, De Boelelaan 1105, 1081
HV Amsterdam, the Netherlands

<sup>34</sup>Institute of Bio- and Geosciences: Agrosphere (IBG-3), Forschungszentrum Jülich,
Wilhelm-Johnen-Straße, 52428 Jülich, Germany

<sup>35</sup>Faculty of Agronomy and Forest Engineering, Eduardo Mondlane University, 3453 Avenida Julius Nyerere,
Maputo, Mozambique

<sup>36</sup>Faculty of Forestry, Bogor Agricultural University, Kampus IPB, Dramaga, Bogor, Indonesia

<sup>37</sup>European Commission Joint Research Centre, European Commission, Rue du Champ de Mars 21,
1050 Brussels, Belgium

<sup>38</sup>NASA Ames Research Center, P.O. Box 1 Moffett Field, CA 94035-1000, USA

<sup>39</sup>Upper ASEAN Wildland Fire Special Research Unit, Kasetsart University, 50 Ngamwongwan Rd, Lat Yao,
Chatuchak, Bangkok 10900, Thailand

<sup>40</sup>Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, the Netherlands

<sup>41</sup>Forest Fire Laboratory, Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization
(DIMITRA), Terma Alkmanos, Ilisia, 11528, Athens, Greece

★These authors contributed equally to this work.

**Correspondence:** Matthew W. Jones (matthew.w.jones@uea.ac.uk), Douglas I. Kelley (doukel@ceh.ac.uk), Chantelle A. Burton (chantelle.burton@metoffice.gov.uk), and Francesca Di Giuseppe (francesca.digiuseppe@ecmwf.int)

Received: 2 June 2024 – Discussion started: 13 June 2024

Revised: 25 July 2024 – Accepted: 29 July 2024 – Published: 14 August 2024

**Abstract.** 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 km² 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 https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) and https://doi.org/10.5281/zenodo.11420742 (Kelley et al., 2024a).

# 1 Introduction

## 1.1 Background

The potential for wildfires is growing under climate change, with increases in the frequency and intensity of drought and periods of fire-favourable weather driving reductions in vegetation (fuel) moisture and priming landscapes to burn more regularly, severely, and intensely (Seneviratne et al., 2022; UNEP, 2022a; Jones et al., 2022; Abatzoglou et al., 2019; Cunningham et al., 2024a). Additionally, human activities and land-use change can contribute to or exacerbate the risk of extremely large, fast-moving or intense fires, especially in tropical forests where people are the primary cause of ignition and forest degradation (Lapola et al., 2023). Recent years have been marked by a series of extreme wildfire events spanning the globe, with record levels of burned area (BA) occurring in the 2019–2020 Australian “Black Summer” bushfires (Abram et al., 2021) and a series of high-ranking wildfire seasons occurring in quick succession in the western United States (2020 and 2021; Higuera and Abatzoglou, 2021), Siberia (2020 and 2021; Zheng et al., 2023), Europe (2022; European Commission Joint Research Centre, 2023), and South America (2019, 2020; Kelley et al., 2021; Ferreira Barbosa et al., 2022; Silveira et al., 2020). The 2023–2024 fire season was marked by unprecedented fire extent and emissions in Canada; deadly fast-moving fires in Hawaii and Chile; the largest individual wildfires on record in the European Union and Canada; and widespread fires in northwestern South America including parts of Amazonia such as Brazil, Bolivia, Colombia, and Venezuela (Mataveli et al., 2024; Kolden et al., 2024; European Commission EU Science Hub, 2023).

The prominence of recent extreme wildfires and wildfire seasons notably contrasts with overall trends in the area burned by fires globally. Due mostly to a reduction in the global savannahs tied to landscape fragmentation and changing rainfall patterns, global BA has fallen since the beginning of this century by around one-quarter (Andela et al., 2017; Jones et al., 2022; Chen et al., 2024). Critically, this decline in fire extent masks major shifts in the distribution of fires globally, with regions such as eastern Siberia and the western United States and Canada experiencing a more than 40% increase in BA since 2000 (Jones et al., 2022; Zheng et al., 2021) and regions such as southeast Australia also showing significant increases over longer periods (Canadell et al., 2021). Likewise, there have been shifts in the global distribution of BA and fire carbon (C) emissions from non-forests to forests globally and from the tropics to the extratropics (Kelley et al., 2019). Hence, focussing on global aggregated BA extent underplays the scale and magnitude of changes in wildfire activity and impact on regional levels. An increase in forest and peatland burning is particularly concerning due to the rich ecosystem services that these regions provide, including C storage and biodiversity (UNEP, 2022b). The intensification of fire regimes in environments that are less fire-adapted is particularly important because these ecosystems are expected to be least resilient to such changes (Grau-Andrés et al., 2024).

The extreme wildfire events of recent years have significantly impacted society and ecosystems across the globe (Cunningham et al., 2024a). Since 1990, wildfire disasters have directly killed or injured at least ~ 18 000 people, a conservative measure based on incomplete records and reporting biased to the global northern countries (updated from Jones et al., 2022; Centre for Research on the Epidemiology of Disasters, 2024). In 2023, 232 000 people were evacuated due to
wildfires in Canada alone (Jain et al., 2024; Kolden et al., 2024). Also, since 1990, fires are estimated to have caused on the order of 10 million premature deaths globally through degraded air quality (Johnston et al., 2012). Degraded air quality related to fires is experienced most strongly in the tropics (Pai et al., 2022) and often disproportionately affects Traditional communities with poor public services or means of protection (Carmenta et al., 2021). Yet, images of North American cities blanketed in smoke during the 2023 fire season highlight the global nature of this problem.

As anthropogenic emissions of CO₂ remain persistently high, the world's natural C sinks in forests, peatlands, and other ecosystems are increasingly pivotal to moderating increases in atmospheric CO₂ concentration (Friedlingstein et al., 2023). Intact forests are often relied upon for delivering national plans for reaching net zero (Smith et al., 2023) and offering sites for nature-based solutions. Yet, massive wildfire emissions from boreal forests and soils in Siberia and Canada across the years 2020, 2021, and 2023 amount to over 1 × 10⁹ t C, a gross flux comparable in magnitude to annual CO₂ emissions from fossil fuel combustion in India, the EU27 or the United States (Friedlingstein et al., 2023; Zheng et al., 2023). While in a natural fire regime these gross emissions should be recuperated through post-fire recovery, the greater vegetation mortality and loss of ecosystem function associated with more widespread and severe fires can also contribute to shifts in local to regional terrestrial C budgets from sinks to sources (Zheng et al., 2021; Gatti et al., 2021; Nolan et al., 2021a; Phillips et al., 2022; Harrison et al., 2018; Cunningham et al., 2024b). Loss of vegetation during extreme fire seasons can also have wider lasting effects on ecosystems, for instance by reducing the habitat area available to endemic species (Ward et al., 2020).

Extreme fires can moreover impact the livelihoods of various communities and landowners who depend on intact natural landscapes. For example, the lands and territories of Traditional communities and Indigenous peoples can be degraded and transformed by wildfires, raising climate justice issues (Garnett et al., 2018; Barlow et al., 2018; Lapola et al., 2023). Further, conflating the detrimental impacts of wildfires types has also stigmatised small-scale intergenerational fire use and led to prohibitive fire governance that affects local communities (Carmenta et al., 2021; Barlow et al., 2020).

Mitigating and adapting to increases in wildfire potential are growing priorities of policymakers and require coordination with many other stakeholders. National and international disaster management centres are seeking to enhance predictive capacity, while fire management agencies are expanding or re-allocating their resources to rapidly suppress fires to avoid them becoming too large, fast, or intense. A number of international organisations such as the UN Environment Programme (UNEP, 2022a), the World Bank (2020, 2024), and the Organisation for Economic Co-operation and Development (OECD, 2023) as well as a range of other inter- or non-governmental organisations are producing reports that con-

solidate evidence on the changing risk of extreme fires and identify best practices for mitigating their impacts, including through land management and urban/rural planning. Many land managers are developing and implementing approaches such as fuel reduction, a process subject to permit systems issued by regional fire management agencies in some countries (Fernandes and Botelho, 2003; Stephens et al., 2012; Moreira et al., 2020; Chuvieco et al., 2023). Wildfire response agencies are exploring innovative approaches to detecting and responding to fires, and there is rising interest in the prospect of integrated fire management around the world (Food and Agriculture Organization of the United Nations, 2024). Operators of C market projects and forest carbon-conservation initiatives, such as REDD+, are particularly wary of the risks that wildfires present to the permanence of C offsets, which often feature as a key tool in national policies and international initiatives for achieving net zero emissions (Barlow et al., 2012; Smith et al., 2023).

Amidst extreme wildfires and wildfire seasons, stakeholders increasingly turn to scientists for answers to pressing questions that naturally arise. How extreme was this fire event in a historical context? Is climate change to blame? Will we see more wildfires like this in the future? Did land management exacerbate or ameliorate the problem? Can we predict events like this in future to improve early warning? What is the role of climate policy in reducing risk of extreme wildfires in future?

While observational, statistical, and modelling tools for assessing extreme wildfire drivers and predicting wildfire occurrence are advancing rapidly, their application to studying extreme wildfire seasons or events on timescales relevant to public and political interest remains limited. The State of Wildfires report represents a new initiative to routinely catalogue extreme wildfire events at annual frequency and explain their occurrence and relation to climate change. The report incorporates recent methodological advances in disentangling the drivers of selected extreme wildfire events to fuel dryness, fuel load, and weather, and ignition and suppression factors. By applying these methodological advances in conjunction with models of global change, we quantify the change in likelihood of the past year's events under climate and land-use changes. Observable fire metrics (e.g. burned area) are the target variable of our causal inference and attribution work, which thereby advances on more common climate attribution studies that attribute change in fire-favourable meteorological conditions to climate change. Overall, this report capitalises on recent advances in the study of extreme fire events and seasons to provide timely information about shifting fire regimes and their causes. The findings of the report are relevant to policymakers, the media, and the wider public.
## 1.2 Objectives of this report

This inaugural edition of the State of Wildfires report aims to stimulate development of tools for understanding and predicting extreme fires and to deliver actionable information to policy and practice stakeholders and wider society. In this edition, we do the following:

1. regionally identify extreme individual wildfires or extreme wildfire seasons of the period March 2003–February 2024 and place them in the context of recent trends;

2. shortlist a selective number of extremes (extreme individual wildfires or extreme wildfire seasons) with notable impacts on society or the environment, which we term the “focal events” in this report;

3. diagnose the contributions of fuel dryness, fuel load, ignitions, and suppression to the occurrence of each focal event;

4. assess the capacity of operational predictive systems to predict each focal event;

5. attribute each focal event to anthropogenic factors including climate change and land use;

6. provide an outlook on the probability of extreme events in the coming fire season (that commenced on March 2024); and

7. project future changes in the probability of each focal event under future climate scenarios.

Key methodologies used to achieve the above objectives are summarised as follows. To address objectives 1 and 2, we build a comprehensive dataset of fire metrics including BA, fire counts, fire C emissions, and individual fire properties (size and rate of growth) for consistent world regions, and we quantitatively identify anomalies in these metrics during the past fire season (Giglio et al., 2018; van der Werf et al., 2017; Andela et al., 2019). To address objective 3 and 4, we leverage seasonal to sub-seasonal forecasts of fire weather from the European Centre for Medium-Range Weather Forecasts (ECMWF) and additionally employ two state-of-the-art fire models, “Controls on Fire” (ConFire) and “Probability of Fire” (PoF) (Kelley et al., 2019; McNorton et al., 2024) to pinpoint the causes of the extreme fire events of 2023–2024. To address objective 5, we employ projections of fire weather from the Hadley Centre Large Ensemble to attribute change in the Fire Weather Index (FWI) to climate change, and we drive ConFire (Kelley et al., 2019) with outputs from the Joint UK Land Environment Simulator Earth System model (JULES-ES; Mathison et al., 2023) to attribute extreme BA to climate and land-use changes (Burton et al., 2024). To address objective 6, we consult predictions of the state of climate modes relevant to fire and present seasonal predictions

of the FWI from the ECMWF (Di Giuseppe et al., 2024). To address objective 7, we again pair ConFire (Kelley et al., 2019) with JULES-ES (Mathison et al., 2023) to project future changes in BA under several future climate and land-use scenarios and provide comprehensive assessment of past and future extreme wildfire events.

The State of Wildfires report will be an annually recurring report that can harness and adopt new methodologies brought forward by the scientific community between the annual iterations of the report. Over the coming years and decades, we aim to enhance the tools presented in this report for application in near-real time, thus enhancing our capacity to transfer key insights to decision-makers at the time they most need it.

# 2 Extreme wildfire events of 2023–2024

## 2.1 Methods

We catalogued the extreme regional wildfire events or annual fire seasons in the period March 2023–February 2024 based on a combination of anomalies in the distribution of several observable fire metrics from Earth observations (Sect. 2.1.1). In this work, the global fire season is defined as occurring in March–February windows oriented around the annual minima of global fire activity in boreal spring (see further details in “Uncertainties” in Sect. 2.1.1).

Due to the diversity of environmental settings in which fires occur and the range of ecological, economic, or societal impacts caused, defining an extreme fire or an extreme fire season remains inherently challenging. To date, extreme fires have commonly been defined by their BA extent, by their feedback on the global climate, and by their socioeconomic impact. While an extreme fire event or extreme fire season may be visible as a significant anomaly against historical Earth observations, the scientific community seeks to apply a more comprehensive definition of extreme fire, including its impacts on society and the environment. To catalogue extreme events that were not necessarily visible in Earth observations, regional expert panels were constructed and given responsibility for identifying extreme events of the past fire season (Sect. 2.1.3). The expert panels were given flexibility to identify and catalogue wildfire characteristics or impacts that are considered regionally extreme but are not necessarily captured by Earth observations. Examples of extremes that can be captured by expert assessment (but not by Earth observations) include suppression challenge; fatalities and structure loss; impacts on human health and wellbeing; impacts on agricultural and other economic sectors; impacts on biodiversity; and impacts on diverse ecosystem services such as recreation, tourism, or other cultural values. Hence, Sect. 2.2 identifies a variety of impactful events displaying a broad range of characteristics and impacts that can occur across diverse fire regimes (e.g. Archibald et al., 2009; Cunningham et al., 2024b; Keeley, 2009).
## 2.1.1 Earth observations of fire

### Input datasets

We assembled observations of burned area (BA), synonymous with fire extent, for the period March 2002–February 2024 from the National Aeronautics and Space Administration (NASA) product MCD64A1 (collection 6.1). MCD64A1 provides daily BA observations at 500 m spatial resolution with global coverage and is based on retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors mounted to the Terra and Aqua satellites (Giglio et al., 2018, 2021).

We also produced a global record of individual fires for the period March 2002–February 2024 by updating the Global Fire Atlas (Andela et al., 2019; Andela and Jones, 2024) through February 2024, driven by the 500m MODIS BA data. The Global Fire Atlas algorithm clusters burned cells into individual fires, tracks their daily progression, and logs attributes such as fire size and mean daily rate of growth. Our updates are provided by Andela and Jones (2024). The Global Fire Atlas is one of several products tracking daily fire progression and identifying individual fires at global scale based on moderate resolution satellite data (Andela et al., 2019; Laurent et al., 2018; Artés et al., 2019). The product uses the MODIS BA product, the smallest unit of disaggregation is 500 m, and the shortest time step on which the expansion of a fire can be observed is daily. Given its resolution, the Global Fire Atlas is expected to represent the dynamics of large fires better than small, fast-moving fires.

### Uncertainties

In addition, we gathered estimates of fire carbon (C) emissions for the period March 2023–February 2024 from two models driven by Earth observations of active fires or burned area: firstly, the Global Fire Assimilation System (GFAS) product, provided operationally by the Copernicus Atmospheric Monitoring Service (CAMS) at 0.1° spatial resolution and daily temporal resolution (Kaiser et al., 2012; European Centre for Medium-Range Weather Forecasts, 2024), and, secondly, the Global Fire Emissions Database (GFED; version 4.1s) product at 0.25° spatial resolution and daily temporal resolution (van der Werf et al., 2017). GFAS is driven by the fire radiative power (FRP) retrievals in the MODIS active fire product MCD14A1 and biome-level relationships between FRP and biomass consumed based on GFED3 (Kaiser et al., 2012). For the 1997–2016 period, GFED4s is driven by MODIS BA data (MCD64A1 collection 5) supplemented with small fire BA based on MODIS active fire data and a model for biomass productivity and fuel consumption (van der Werf et al., 2017). For the post-2016 period, emissions are based on active fire detections scaled to emissions using pixel-based scaling factors derived from the 2003–2016 overlapping period.

We note that the MODIS BA product data used in our analyses of anomalies in BA and individual fire properties (via the Global Fire Atlas) are known to be conservative due to the limitations to detecting small fires (e.g. agricultural fires) based on surface spectral changes at 500 m resolution. Recent work has shown that including detections of small active fires increases global BA estimates by 93% (Chen et al., 2023). However, variability and trends in regional BA totals using datasets that include small fires do not differ significantly from the variability and trends present in the MODIS BA product (Chen et al., 2023). Hence, inclusion or exclusion of small fires tends to generate biases in central estimates of BA in one direction or the other, in line with the sensitivity of different sensors to different fire types. Uncertainty in the detection of small fires is larger than in the case of fires detected in the MODIS BA product, due to limited validation (van der Werf et al., 2017). The MODIS BA product with resolution of 500 m is deemed highly suitable for addressing the research questions of this report, which focus on more impactful fires that tend to burn larger areas.

Uncertainties in fire carbon emissions estimates from GFED4.1s are on the order of ±20%–25% at 1 SD for global totals (van der Werf et al., 2017, 2010). Uncertainties in GFED4.1s are made up of uncertainties in BA, the amount of biomass consumed per unit BA, and the carbon emitted per unit biomass burned. Revisions to BA input data, discussed above, have tended to influence GFED central estimates of fire C emissions to a greater degree than the uncertainties around central estimates (van der Werf et al., 2017; Chen et al., 2023). Uncertainties in fire carbon emissions estimates from GFAS are on the order of approximately ±25% at 1 standard deviation for global totals. Uncertainties are introduced by missed active fire detections, either below the detection threshold of the MODIS instruments or not observed during the limited diurnal coverage of low-Earth-orbiting satellites, assumptions made for biome classifications, coefficients used to convert observed thermal anomalies to consumed dry matter, and emission factors used to estimate emitted quantities of carbon and pyrogenic pollutants. Variation in C emissions estimates on the order of approximately 20%–60% has been observed in studies comparing multiple emissions products (Wiedinmyer et al., 2023).

### Regional burned area, carbon emissions, and fire count totals

We calculated regional totals of BA and C emissions based on a variety of regional layers defined in Table 1. The regional layers represent a range of biogeographical boundaries (e.g. biomes), geopolitical boundaries (e.g. countries), and values used in scientific reports (e.g. by the Intergovernmental Panel on Climate Change, IPCC). We calculated monthly totals of BA and fire C emissions for each region by aggregating monthly BA and daily C emissions data and summing the data from the input datasets both spatially and
temporally as required. In the case of fire C emissions, we
also calculated the mean estimate of fire C emissions from
GFED4.1s and GFAS, regionally.

We adopt a March–February definition of the global fire
season (e.g. the latest global fire season spans March 2023–
February 2024). Due to an annual lull in the global fire calen-
dar in the boreal spring months, fire season BA totals are least
sensitive to the shifts in fire season cutoffs of 1–2 months if
the fire season centres on spring (Boschetti and Roy, 2008).
This makes the global fire season centred on spring a prag-
matic option for the study of interannual variability or trends
in fire extent (Boschetti and Roy, 2008). The period March–
February is specifically oriented at the end of the austral fire
season and before widespread fires have begun in the bo-
real extratropics. The regions where this global definition of
the fire season is most problematic are northern hemispheric
South America, southeast Asia, and central America (Giglio
et al., 2013).

In addition, we calculated totals of regional fire counts for
each global fire season based on the number of individual
fire ignition points present within each region, using ignition
point vectors from the Global Fire Atlas. The resolution of
the MODIS data supplied to the Global Fire Atlas algorithm
is 500 m, and hence fires that are smaller in scale are omitted.
Regional or national systems may record greater fire counts
due to the inclusion of smaller fires.

## 2.1.2 Identifying extreme fire seasons and events from Earth observations

### Regions with extreme wildfire seasons

Anomalies in BA, fire C emissions, and fire counts in the latest global fire season (March 2023–February 2024) were calculated in several ways:

i. as relative anomalies (expressed in %) from the annual mean during all previous March–February periods since 2002 (2003 for fire C emissions);

ii. as standardised anomalies (standard deviations) from the annual mean during all previous March–February periods since 2002 (2003 for C emissions);

iii. as a rank amongst all March–February periods since 2002 (2003 for fire C emissions) and March 2023–February 2024 inclusive.

In this report, anomalies in fire C emissions are reported
based on the two-model mean estimate from GFED4.1s
and GFAS; however anomalies based on the GFED4.1s or
GFAS estimates individually are also available via Jones et
al. (2024).

We identified regions in which the latest fire season was
potentially classifiable as “extreme” based on the rank of BA,
C emissions and fire count amongst all fire seasons. For vi-
sualisation purposes, we identified regions in which the lat-

est fire season ranked in the top five of all annual fire sea-
sons on record (see Sect. 2.2.1). The BA data for the period
March 2002–February 2024 include 23 fire seasons, while
the C emissions data for the period March 2003–February
2024 include 21 fire seasons. Hence, a top-five ranking trans-
lates approximately to a fire season in the upper quartile of
those on record.

We further characterised the onset, peak, and cessation
of anomalous monthly BA in March 2023–February 2024.
First, we identified the month of the event's peak as the
maximum difference between monthly BA values in March
2023–February 2024 and the climatological mean monthly
values from the prior March–February periods. Thereafter,
the event's onset and cessation were defined as the bounds
of consecutive months with above-average BA prior to and
following the peak but limited to the March 2023–February
2024 period.

### Regions with extreme individual wildfire attributes

We identified regions in which large or fast-moving fires oc-
curred in the latest fire season based on records of individual
fires from the Global Fire Atlas. For each region (Table 1)
and year, we estimated the size of the largest fire, the daily
rate of growth of the fire that spread most rapidly, the size of
the 95th percentile fire, and the daily rate of growth of the
95th percentile fire. In the Global Fire Atlas, the daily rate of
growth for any given fire is determined by calculating the av-
erage daily rate of growth at which the fire advanced across
all its constituent cells. This method includes cells burned by
the head, flank, and backfire and produces lower spread rates
than if the calculation were based solely on the cells burned
by the head fire.

Anomalies in each fire attribute were calculated using the
same metrics as for BA (see (i)–(iii) above), and we identi-
fied regions in which the latest fire season featured fires with
potentially extreme attributes based on the rank of BA and
fire C emissions amongst all fire seasons.

## 2.1.3 Identifying extreme fire seasons and events by expert consultation

We assembled a panel of regional experts (two from each
continent, Table A1), to contribute to the identification, de-
scription, and characterisation of extreme wildfire seasons or
impactful events in the latest fire season. A key role of the
expert panel was to catalogue regional events that signifi-
cantly impacted society or the environment but which may
not have been detected by Earth-observing satellites due to
issues such as scale, short duration, timing of overpass, and
cloud or canopy cover. This includes (but is not limited to)
wildfires that impacted society by causing fatalities, evacua-
tions, displacement (e.g. homelessness), direct structure or
infrastructure loss or damage, degradation of air or water
quality, loss of livelihood, cultural practice or other ways
<table><caption>**Table 1.** Regional layers to which global Earth observations were disaggregated and used to define regions with extreme wildfire seasons or extreme individual wildfire attributes. Regional layers are available from Jones et al. (2024). n/a – not applicable</caption><thead><tr><th>Layer</th><th>Short form</th><th>Source</th><th>Notes</th></tr></thead><tbody><tr><td>Biomes</td><td>n/a</td><td>Olson et al. (2001)</td><td></td></tr><tr><td>Continents</td><td>n/a</td><td>ArcGIS Hub (2024)</td><td></td></tr><tr><td>Continental biomes</td><td>n/a</td><td>See above</td><td>Spatial intersect of biomes and continents.</td></tr><tr><td>Countries</td><td>n/a</td><td>EU Eurostat (2020)</td><td></td></tr><tr><td>UC Davis Global Administrative Areas (GADM) level 1</td><td>GADM-L1</td><td>UC Davis (2022)</td><td>First sub-national administrative level, such as states of the United States or provinces of China. Version 4.1.</td></tr><tr><td>Intergovernmental Panel on Climate Change Sixth Assessment Report (AR6) Working Group I (WGI) reference regions</td><td>IPCC AR6 WGI regions</td><td>Iturbide et al. (2020)</td><td></td></tr><tr><td>Global C Project Regional C Cycle Assessment and Processes (RECCAP2) reference regions</td><td>RECCAP2 regions</td><td>Ciais et al. (2022)</td><td></td></tr><tr><td>Global Fire Emissions Database (GFED) basis regions</td><td>GFED4.1s regions</td><td>van der Werf et al. (2006)</td><td></td></tr></tbody></table>

of life, and loss of economic productivity. This definition also includes (but is not limited to) wildfires that impact the environment via disturbance to vulnerable ecosystems, biodiverse areas, or ecosystem services such as C storage. This approach recognises that Earth observations do not provide a complete record of all impactful fires. We do not define ubiquitous quantitative thresholds of impact by any of the measures outlined above but rather invite in-region experts to identify events that triggered impacts that were sufficient in magnitude to infiltrate public and political discourse. The sources of information available for cataloguing regional events include national/regional fire records, fire service reports, disaster management reports, news reports, and social media. A second key role of our expert panel was to describe and contextualise the impacts of the fire seasons highlighted as extreme by Earth observations or regional assessment (see Sect. 2.2.3).

The year in review by continent, produced by the expert panel, is presented in Appendix A.

### 2.1.4 Context of recent extremes: regional trends in burned area

To place recent extremes in the context of fire trends of the past 2 decades, we update our regional analyses of trends in annual BA from Jones et al. (2022). In contrast to Jones et al. (2022), we present trends that align more closely with global fire seasons, spanning the period March 2002–February 2024 rather than trends over calendar years. We

quantified trends using the Theil–Sen slope estimator, which is useful when data may contain outliers or be non-normally distributed, making it less sensitive to outliers than a standard least-squares regression slope. Changes were calculated by multiplying trends (per year, yr⁻¹) by the number of fire seasons in the period of coverage for each variable (Sect. “Uncertainties”). Relative changes were calculated as the absolute changes divided by the mean annual BA during the period following Jones et al. (2022) and Andela et al. (2017). The significance of trends was evaluated using the Mann–Kendall test, with a confidence level set at 95%.

In addition to reporting trends in *total* BA, we also present trends in *forest* BA as these regularly diverge from total BA trends (see Sect. 2.2.2). Forest BA is calculated as described in Sect. 2.1.1 but after isolating burned cells in areas with tree cover exceeding 30% in NASA’s annual MODIS MOD44B collection 6.0 Continuous Vegetation Field product (250 m) (DiMiceli et al., 2015). The 30% threshold is widely used amongst studies of forest cover change (e.g. Li et al., 2017; Cunningham et al., 2020; Sexton et al., 2016).

### 2.1.5 Shortlisting of focal events

In later sections of this report, we conducted various analyses to understand the causes and predictability of a selection of extreme wildfire seasons or events during March 2023–February 2024 (see Sects. 3–5). We limited the number of analyses to three globally prominent focal events of the 2023–2024 global fire season because the approaches used
are not operational, and time is required to train and optimise
our models regionally.

In discussion with our expert panel, we prioritised the three events studied in this report by weighing up the anomalies in Earth observations during the latest fire season as well as the impacts that these extremes had on people and the environment. The focal events are notable for their international significance, attracting attention from the media and policymakers both within and beyond their region.

## 2.2 Results

### 2.2.1 Extreme fire seasons and events of 2023–2024

#### Extreme fire seasons from Earth observations

According to the MODIS BA product, $3.9 \times 10^6$ km$^2$ burned globally during the 2023–2024 global fire season (March 2023–February 2024), slightly below the average of previous fire seasons ($4.0 \times 10^6$ km$^2$) and overall ranking 12th of all fire seasons since 2002 (Jones et al., 2024). Despite this, fire C emissions were 16% above average at 2.4 Pg C during the 2023–2024 global fire season, which ranks seventh amongst all fire seasons since 2003 (based on annual averages of GFED4.1s and GFAS estimates; see Methods; Jones et al., 2024).

Stark regional contrasts in the anomalies in BA, fire C emissions and individual fire properties are visible in the Earth observations on various regional scales (Figs. 1, 2, 3). Figure 1 shows the strongest BA and fire C emissions anomalies of 2023–2024 at continental biome scale versus previous fire seasons. BA was around 300 000 km$^2$ (13%) below the average of previous fire seasons in the African grassland, savannah, and shrubland biome, which is globally significant because the continental biome contributes 58% towards the global total BA in the average year up to February 2023 (Jones et al., 2024). BA was also around 17% below average in the South American grassland, savannah, and shrubland biome in 2023–2024 and in Asian non-forest biomes. In contrast, BA was 26% above the average of fire seasons since 2002 in the Australian grassland, savannah, and shrubland biome (Figs. 1, 2). Collectively, these three biomes contributed 71% of total BA in the global total BA in the average fire season between March 2002 and February 2023, and so departures from average values are particularly impactful on global BA totals.

The North American boreal forests experienced a record-breaking fire season, with BA reaching 6 times the average since 2002 and fire C emissions reaching over 9 times the average since 2003 (Fig. 1; Jones et al., 2024). This strong regional signal primarily explains the above-average global C emissions total of 2023–2024, with the high rates of fire emissions per unit area in boreal forests aggregating to override the reduced emissions totals in African and South American savannahs. Record levels of fire C emissions were also seen across the global pan-boreal forest biome, with fire C

emissions surpassing the pan-boreal record set in 2021 by more than 60%. This is despite a below-average fire season for BA and fire C emissions in boreal Asia during 2023–2024, in contrast to the 2021–2022 fire season, when there was a synchronous peak in BA in both the Eurasian and North American boreal regions (Zheng et al., 2021). According to the Global Fire Atlas, new records for individual fire size and rate of spread were also set in the North American boreal forests during 2023–2024, while 95th percentile fire size and rate of growth in 2023–2024 were in the top 2 and 3 years on record since 2002, respectively (Jones et al., 2024). Overall, the Canadian boreal forests contributed 24% towards total fire C emissions in the 2023–2024 fire season, up from 3% in an average fire season since 2003.

Anomalies in the African (sub-)tropical grasslands, savannahs and shrublands strongly drive inter-annual variability in global fire C emissions because this biome contributes on average 40% towards total global fire C emissions (Jones et al., 2024). If fire C emissions from African (sub-)tropical grasslands, savannahs and shrublands had been around average fire season in 2023–2024, then global fire C emissions would have been the greatest of any fire season on record since 2003.

Elsewhere at the biome scale, BA extent was in the top 3 years on record in the South American broadleaf and mixed forests; the African xeric shrublands; the Australian xeric shrublands; and the Australian (sub-)tropical grasslands, savannahs, and shrublands (Fig. 1). In contrast, BA or fire C emissions were the lowest on record in the European temperature broadleaf and mixed forests and Asian xeric shrublands and in the bottom 3 years on record in the African savannahs, Asian montane grasslands and shrublands, and European tropical grasslands and shrublands.

On national levels, the most prominent global anomaly of the 2023–2024 fire season occurred in Canada, where BA reached 6 times the average of previous fire seasons and fire C emissions reached 9 times the average of previous fire seasons. Across the Canadian provinces and territories, the highest BA or fire C emissions on record were observed in Northwest Territories, British Columbia, Alberta, and Quebec, while Yukon, New Brunswick, and Ontario also experienced high-ranking years (Figs. 2, 3). The positive BA anomalies in Canada were visible in the MODIS BA dataset from as early as April 2023 in most provinces and persisted throughout summer through to October and even through to December 2023 and January 2024 in British Columbia and Alberta (Fig. S2). Peak anomalies were observed in eastern Canada in June 2023, arriving later in western Canada (August–September). Data on individual fire characteristics from the Global Fire Atlas further reveal new record fire counts in many Canadian provinces and high-ranking anomalies in fire count and daily rate of growth across Canada, as well as new records for fire size and rate of spread in provinces of both eastern and western Canada (Fig. 4; Jones et al., 2024). Appendix A discusses the unprecedented Cana-
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Anomalies in BA and C (C) emissions for selected continental biomes in the 2023–2024 global fire season (March 2023–February 2024) versus the average of prior fire seasons since 2002. The selected regions all contribute at least 0.1 % towards global mean annual BA and experienced BA anomalies of over ±30 000 km² in the 2023–2024 global fire season. Relative changes (%) are also marked by triangular symbols and can be read on the same scale as the absolute values.</figcaption></figure>

dian fire season of 2023–2024 in greater detail, including its impacts and regional context.

A second prominent regional feature of the 2023–2024 global fire season, visible in Earth observations, is a cluster of administrative regions with positive BA and C emissions anomalies in the north and west of tropical South America (Figs. 2, 3). Bolivia, Guyana, Suriname, French Guiana, Honduras, Nicaragua, and Belize all experienced a high-ranking fire season at a national level in 2023–2024. In addition, BA or fire emissions were ranked in the top 3 years in western parts of Amazonia including in the State of Amazonas of Brazil, the Loreto Department of Peru, and the La Paz and Beni departments of Bolivia. Anomalies in the western Amazon spanned June 2023–January 2024, peaking in August–October 2023. In the north of South America, high-ranking fire seasons were seen in Venezuela; in various subdivisions of Guyana, Suriname, and French Guiana; and in the State of Amapá in Brazil. The anomalies in northern South America spanned May 2023–February 2024, peaking in November 2023–February 2024 (Fig. S2). The Global Fire Atlas data suggest that South American anomalies in BA during the 2023–2024 fire season were principally driven by a large number of fires, whereas anomalies in fire size or rate of

growth were uncommon in most of South America (Fig. 4). Appendix A discusses the 2023–2024 fire season of tropical South America and its impacts and regional context in greater detail.

Several parts of south and southeast Asia experienced high-ranking anomalies in BA or fire C emissions during the 2023–2024 fire season, including various neighbouring administrative zones of Lao People’s Democratic Republic (PDR), Thailand, and Vietnam. The temporal peak of these anomalies was broadly in March–May 2023. Data on individual fire characteristics indicate that high-ranking fire counts, rather than anomalies in fire size, were the primary driver of the regional BA anomalies (Fig. 4). Appendix A discusses these anomalies and their impacts in greater detail.

The anomalies observed in xeric biomes of Oceania are also apparent as high-ranking BA or C emissions in the 2023–2024 fire season in western parts of Australia, particularly in Western Australia and the Northern Territory (Figs. 2, 3). Fires tended to affect more remote areas, and so the impacts on society were muted in comparison to the Black Summer events affecting southeast Australia in 2019–2020 (Abram et al., 2021); however, Appendix A discusses some notable exceptions.
Other regional pockets of high-ranking BA anomalies or C emissions anomalies were observed in various dry zones of Africa and the Middle East, including the Sahel, northern Africa and the Horn of Africa, southern Africa (specifically South Africa and Botswana, where a period of 3 high rainfall years has resulted in grass fuel accumulation), parts of Iran and Iraq, parts of the Levant region, and parts of the Arabian Peninsula (Figs. 2, 3). Although various aspects of the fire season ranked highly in these regions, they are also fuel-limited with a generally a low baseline for BA and fire C emissions and the wildfire season. Nonetheless, regionally impactful wildfires were reported for Algeria, Tunisia, and Morocco as well as coastal regions of South Africa and Pakistan, and are discussed further in Appendix A.

## Extreme individual fires from Earth observations

To support our analyses of anomalies in individual fire properties and provide insights into the limitations and uncertainties inherent in global-scale analysis of individual fires, we provide a brief assessment of the skills with which the Global Fire Atlas represents some of the most impactful individual fires of 2023–2024. The Global Fire Atlas represents some of the most impactful individual fire events in 2023–2024 with varying skill (Table 2; Figs. S3, S4, S5). For example, the Evros fire that occurred in the Decentralised Administration of Macedonia and Thrace, Greece, in late August was captured reasonably well. The Global Fire Atlas identifies two fires that ignited on 19 August and merged to form one contiguous burned unit with an area of approximately 900 km². Alignment of the fire’s timing, size, and perimeter with high-resolution satellite imagery (Fig. S3) and detailed reports (Xanthopoulos et al., 2024) suggest an overall reliable representation of this particular event by the Global Fire Atlas. The impacts of this fire are discussed in detail in Appendix A.

A deadly fire near Valparaíso, Chile, is also captured with reasonable skill in the Global Fire Atlas (Fig. S4). Around 90 km² was burned, with the fire skirting the city of Placilla de Peñuelas and encroaching upon Viña del Mar and Quilpué (Appendix A). The timing, extent, and perimeter of the fire as recorded by the Global Fire Atlas compare well with those reported by other sources (Table 2).

Among the largest fires to occur in Canada during 2023–2024 happened near the La Grande Reservoir in Quebec, Canada. According to both the Global Fire Atlas and a separate NASA fire tracking product based on the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor, the fire’s extent was around 11 000 km², whereas the National BA Composite (NBAC; Skakun et al., 2022) shows a similar extent of 10 000 km². The timing of the fire also showed high correspondence across the products.

The Lahaina wildfire in Maui, Hawaii, is an example of an event that was captured poorly by the Global Fire Atlas (GFA). Issues relating to the small scale of this fire relative to the resolution of the MODIS BA data are evident

in Fig. S4. As the MODIS BA algorithm is focussed on the detection of wildland fire, its effectiveness in tracking fires at the wildland–urban interface is limited. In this case, burned areas were not detected in cells in urban areas or at the wildland–urban interface, and hence the size of the fire was under-estimated significantly (Table 2). The timing of the fire on vegetation land adjacent Lahaina was compatible with reference reports.

Another example of the challenges of defining individual fire extent and applying global algorithms to do so comes from Western Australia (Fig. S5). Two fires recorded by the Department of Fire and Emergency Services, Western Australia (the Great Sandy Desert and Anna Plains fires) totalled 57 000 km² in extent. In the Global Fire Atlas, the burned cells detected by MODIS were instead dissected into 53 separate fires, with the largest unit burning 560 km². The date ranges were also rather different, with the first record of fires logged in agency data 1 month later than in the Global Fire Atlas and the final record logged 1 month earlier.

## 2.2.2 Context of recent extremes: regional trends in burned area

The anomalies of 2023–2024 occur against a backdrop of trends in BA this century that point towards shifts in fire regime. Figure 5 shows significant trends in BA and forest BA across the fire seasons in the period March 2002–February 2024 derived from MODIS BA data. While many world regions are experiencing declines in total BA, increases in forest BA are far more prevalent than declines at the scale of continental biomes, countries, and administrative regions.

Northern hemispheric extratropical biomes in North America and Asia have shown a clear signal towards increased forest BA since 2002, which are also visible on national and provincial scales in Canada, the United States, and Russia and on provincial scales in various states of western and eastern Canada, the western United States, and Siberia. These trends occasionally propagate to trends in total BA, for example in western and northern Canada and in the Sakha Republic (eastern Siberia). The large 2023–2024 anomalies in BA in Canada align with the doubling of forest BA in Canada across fire seasons that have occurred since 2002 (a significant trend, *p* < 0.05) and a 23% increase in total BA in Canada (marginally significant at *p* < 0.1). Three Canadian provinces showed significant increases in both total and forest BA this century: British Columbia (+35%–42%), Northwest Territories (+55%–68%), and Yukon (+60%–135%). No Canadian provinces experienced a significant decline in forest BA or total BA. More widely, there has been a 58% increase in forest BA in the North American boreal forest biome since 2002 and a 134% increase across the pan-boreal forest biome of North America and Eurasia. The succession of events affecting boreal forests in Canada in 2023, Siberia in 2020, and both North America and Asia during
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Ranks of BA during March 2023–February 2024 versus previous March–February periods (n = 23 global fire seasons) for three regional layers: (a) continental biomes, (b) countries, and (c) states or provinces. Results for regions with high-ranking (top 5 years) or low-ranking (bottom 5 years) events are highlighted. The timing of BA anomalies is shown in Fig. S2 in the Supplement.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Rank of fire C emissions during March 2023–February 2024 versus all March–January periods since 2003 (n = 21 global fire seasons), at the scale of (a) countries and (b) level 1 administrative regions. We consider C emissions estimates from two products (GFAS and GFED), first calculating the mean emissions value from the two products and then ranking the values.</figcaption></figure>

2021 appears to be part of a continued trend towards rising fire extent in the high latitudes this century.

Elsewhere in the southern hemispheric extratropics, sig-
nificant rises in forest BA have been seen in Chile since
2002 (+87 %), including in the central regions of Araucanía,
Biobío, Maule, Ñuble, and Valparaíso, ranging from 35 to
109 %. Extreme fires in Valparaíso during 2023–2024 and
in Araucanía, Biobío, and Ñuble in the 2022–2023 fire sea-
son follow an extreme 2022–2023 fires season in Maule,
Nūble, Biobío, and Araucanía (Appendix A), consistent with
a longer-term rise in BA in central Chile (Fig. 5). Increases
in BA are not generally significant in fire-prone parts of the

southern hemisphere extratropics, such as Africa or Aus-
tralia.

In the tropics, trends in total and forest BA show a variety
of patterns. While total BA has reduced across much of the
savannah-occupied regions of South America, Africa, and
northern Australia, trends in forest BA (> 30% tree cover)
are far more varied (Fig. 5). Hence, fires in woody tropi-
cal vegetation show a less consistent global trend. In addi-
tion, exceptions to the general decline in total BA across the
tropics are seen in the Brazilian State of Amazonas and the
Congo basin and across much of India (Fig. 5). The trend
in Amazonas, among the most pristine parts of Amazonia,
contrasts with other states of Brazil such as Mato Grosso and
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Ranks of (a) fire count, (b) 95th percentile fire size, and (c) 95th percentile daily rate of growth during March 2023–February 2024 versus all March–February periods since 2002, at the scale of states or provinces (GADM administrative level 1 regions).</figcaption></figure>
<table><caption>**Table 2.** Representation of selected individual fire events in the MODIS BA product (Giglio et al., 2018) and the Global Fire Atlas (Andela et al., 2019).</caption><thead><tr><th>Event</th><th>Global Fire Atlas fire size (km²)</th><th>Dates (Global Fire Atlas)</th><th>Reported area (km²) (reference)</th><th>Dates (reference)</th><th>Reference</th><th>Comment</th></tr></thead><tbody><tr><td>Alexandroupolis wildfire, Evros, Greece</td><td>892</td><td>19 to 30 August 2023</td><td>930</td><td>19 to 31 August 2023</td><td>Xanthopoulos et al. (2024)</td><td>Good characterisation of the event, with two fires merging in the date range and ultimate fire size comparable to reference.</td></tr><tr><td>Fire near La Grande Reservoir, Quebec, Canada</td><td>10 725</td><td>29 May to 23 July 2023</td><td>11 400 (VIIRS) 9694 (NBAC)</td><td>1 June to 23 July 2023</td><td>NASA Earth Observatory (2023; VIIRS); Jain et al. (2024; NBAC)</td><td>Reasonable characterisation of the fire's extent and timing.</td></tr><tr><td>Valparaíso wildfire, Chile</td><td>91.54</td><td>31 January to 10 February 2024</td><td>85</td><td>2 to 5 February 2024</td><td>NASA Earth Observatory (2024); Copernicus Emergency Management Service (2023a)</td><td>Good characterisation of the scale of the event and its perimeters at various wildland-urban interfaces versus reference data.</td></tr><tr><td>Lahaina wildfire, Maui, Hawaii</td><td>1.50</td><td>8 to 12 August 2023</td><td>8.49</td><td>8 to 9 August 2023</td><td>Fire Safety Research Institute (2024)</td><td>MODIS data have coarse spatial resolution relative to scale of event. Spread into urban areas not captured.</td></tr><tr><td>Western Australia (Great Sandy Desert and Anna Plains fires)</td><td>45 544</td><td>31 August to 28 November 2023</td><td>56 561</td><td>27 September to 1 November 2023</td><td>Department of Fire and Emergency Services, Western Australia (shapefile for the Great Sandy Desert and Anna Plains fires; Agnes Kristina, personal communication, 2024)</td><td>The Global Fire Atlas splits this event into 53 fires; we report their total combined area. Largest individual fire area in the Global Fire Atlas was 760 km² (ignited 6 September 2023). Great Sandy Desert and Anna Plains fires merged on 25 October 2023.</td></tr></tbody></table>

Pará, where deforestation rates and deforestation-related fires have fallen since their peak during the early 2000s (Silva Junior et al., 2020). The anomalous fire activity and C emissions in the State of Amazonas during the 2023–2024 fire season (but not other states of Brazil) thus appear to be consistent with the emerging pattern of increased fire in the region. Meanwhile, the 2023–2024 anomalies in BA and other fire properties in the Bolivian, Peruvian, Colombian, and Venezuelan parts of Amazonia (Appendix A) typically occurred against a backdrop of reduced BA or no significant trend in recent decades (Fig. 5).

### 2.2.3 Focal events of this report

#### Canada

In this year's report, the extreme wildfire season in Canada is selected as one of our focal events. It emerges as a major event of global relevance for the following reasons (see Sect. 2.2.1 and the results of the expert consultation presented in Appendix A):

- Record-breaking burned area. The North American boreal forests, particularly in Canada, experienced an unprecedented fire season. The BA reached 6 times the average since 2001.

- High C emissions. Fire C emissions in Canada were over 9 times the average since 2003, contributing significantly to global C emission totals for the year. Canadian boreal forests contributed 24% towards the total above-average global fire C emissions in 2023–2024, up from 3% in an average year.

- Early and persistent fires. Positive BA anomalies were visible from April 2023 (Fig. S9) and persisted through to October, with some regions experiencing fires until January 2024. The fire season lasted nearly a month longer than normal, with the largest 1 d BA total ever recorded in Canada occurring on 22 September 2023.

- Regional anomalies. Peak fire anomalies were observed in eastern Canada in June 2023 and later in western Canada (August–September), indicating widespread and prolonged fire activity across the country.

- Record fire size and spread. New records for individual fire size and rate of spread were set, with many provinces experiencing high-ranking anomalies in fire count and daily growth rates.

- Extensive impact across provinces. The highest BA or fire C emissions on record were observed in Northwest Territories, British Columbia, Alberta, and Que-
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Relative changes (%) in (**a**, **c**, **e**) total annual BA and (**b**, **d**, **f**) forest BA across March–February fire seasons during 2002–2024 for three regional layers: (**a**, **b**) continental biomes, (**c**, **d**) countries, and (**e**, **f**) level 1 administrative regions (e.g. states or provinces). Forest BA only considers areas with tree cover over 30 % at the native (500 m) resolution of the BA observations. Relative changes are calculated as the trend in BA across fire seasons March 2002–February 2022 through March 2023–February 2024 multiplied by the number of years in the time series and divided by the mean annual BA during the period. Trends in BA are derived using the Theil–Sen slope estimator. Only significant trends in BA are shown (dark-grey fill signifies no significant trend).</figcaption></figure>

bec, with other provinces like Yukon, New Brunswick,
and Ontario also experiencing significant fire activity.

- *Air quality impact.* Smoke from these fires led to severe air quality issues, affecting major cities in North America, including New York, which experienced its worst air quality in half a century.

- *Firefighting challenges.* Canada was at its highest national preparedness level for an unprecedented 120 continuous days, indicating the significant resource sharing and international assistance required to manage the fires.

- *Human and economic toll.* Over 232 000 people were evacuated across various regions, and despite the extreme fire activity, no civilian deaths were directly attributed to the fires, showcasing the effective, albeit strained, emergency response efforts.

To assess the causes of specific regional BA anomalies,
four anomalous BA regions–month combinations were cho-
sen across Canada: western Taiga Shield and Taiga bo-
real plains for May and June (includes Alberta and British
Columbia boreal plains and the Montane Cordillera); and
eastern Taiga Shield in Quebec for June and July. Fig-
ure 6 maps the magnitude of anomalies in these regions and
months. Though note that the size and long period of this pro-
tracted event mean that even these regions and months do not
capture all the anomalous BA over Canada in 2023 (Fig. S9).

Greece

In this year’s report, the extreme wildfire season in Greece
is selected as one of our focal events. It emerges as a ma-
jor event of global relevance for the following reasons (see
Sect. 2.2.1 and the results of expert consultation provided in
Appendix A):

- *Second-highest BA on record.* Greece experienced its second-worst fire season in terms of total area burned, with 1727 km² affected, despite recent efforts to strengthen firefighting mechanisms. The 2023 fire season was notably more severe than typical years, with the total BA significantly exceeding the country's historical averages and recent challenging fire seasons.

- *Multiple large fires.* From mid-July to late August, Greece faced numerous large fires that overwhelmed firefighting capabilities. Key fires included those on the island of Rhodes, which burned 207 km², and the massive Evros fire, which reached 938 km².

- *Evros fire disaster.* The Evros fire became the largest on record in recent European history, significantly impacting both forested and agricultural areas. It also led to the tragic deaths of 19 immigrants, who were trapped by the flames.

- *Urban and infrastructural impact.* Fires near populated areas necessitated large-scale evacuations, including 20 000 tourists on Rhodes and multiple settlements around Mount Parnis in Attica. The Evros fire also caused a powerful explosion at an air force base, resulting in damage to the town of Nea Anchialos.

- *Significant evacuations.* Numerous evacuations took place, highlighting the severity of the situation. These included evacuations in Alexandroupolis and its surrounding villages due to the Evros fire.

- *Economic and environmental damage.* The fires caused extensive damage to properties, infrastructure, and natural reserves, with significant impacts on biodiversity and local economies.

- *Firefighting challenges.* The simultaneous spread of multiple fires stretched firefighting resources to their limits, with a notable focus on evacuations rather than fire suppression in some instances.

Abnormally highly burned areas were reported around
Alexandroupolis in August and extended further west across
the administrative region of Macedonia and Thrace. Anoma-
lies were also present in central Greece and around Athens in

July and August (Fig. S10). Figure 6 maps the magnitude of
the anomalies for August.

Western Amazonia

Our final focal event of 2023–2024 is a box drawn in western
Amazonia with bounding coordinates 2.25°N, −56.00°E
and −9.75°S, −77.75°W. It includes Amazonas (Brazil);
Loreto (Peru); and La Paz and Beni (Bolivia), where peak
fire anomalies occurred simultaneously. It emerges as a ma-
jor event of global relevance for the following reasons (see
Sect. 2.2.1 and the results of expert consultation provided in
Appendix A):

- *Record-setting fire activity.* The 2023 fire season in western Amazonia saw unprecedented fire counts, with new records set across the State of Amazonas in Brazil, Loreto Department in Peru, and La Paz and Beni departments in Bolivia.

- *Severe air quality degradation.* Smoke from widespread fires led to significantly degraded air quality across the region, impacting millions of people and posing serious public health risks.

- *Broad socio-economic and health impacts.* The fires caused extensive socio-economic disruptions, including health issues from poor air quality; legal action for inadequate fire prevention; and impacts on livelihoods, particularly for Indigenous and Traditional communities.

- *Widespread environmental degradation.* The fires contributed to significant C emissions and environmental degradation, affecting forest ecosystems and increasing the region's vulnerability to future climatic extremes. Western Amazonia has global significance due to its critical role in C storage and biodiversity and relatively low levels of disturbance.

- *Impact on Indigenous and Traditional communities.* Fires had potential to significantly disrupt the lives and livelihoods of Indigenous and Traditional populations, exacerbating their vulnerabilities due to isolation and reduced access to resources.

Abnormally highly burned areas were reported in western
Amazonia during September and October. Figure 6 maps
the magnitude of these anomalies. The most widespread BA
anomalies emerged in August 2023 and extended through to
November 2023 (Fig. S11).

3 Diagnosing drivers and assessing predictability

3.1 Methods

3.1.1 Predictability of focal extremes

We evaluated the time frame over which extreme events
could have been forecasted using a common metric of fire
<figure><img src="image_8.png" /><figcaption>**Figure 6.** Spatially explicit anomalies in BA fraction (%) during key months for focal events in (a) Canada, (b) Greece, and (c) western Amazonia. Plotted data are the absolute change from the climatological mean BA fraction for the month (%), based on the MODIS BA product aggregated to 0.25°. Red indicates higher BA in that month of 2023 vs the 2002–2022 climatological average for that month. Boxes indicate focus events for our analyses in this report. The top panels show anomalies in Canada for various months, the lower-left panel shows anomalies in Greece for August, and the lower-right panel shows anomalies in western Amazonia during September and August.</figcaption></figure>

danger, the Fire Weather Index (FWI). Developed by the Canadian Forest Service as part of the Canadian Forest Fire Danger Rating System (CFFDRS; van Wagner, 1987), the FWI comprises various components that consider the influence weather on fire danger, with 2 m temperature, 10 m wind speed, precipitation, and 2 m relative humidity as prerequisite variables. The FWI combines three sub-indices, which are

fuel moisture codes representing vegetation moisture state at different layers in the forest floor, as well as a spread index influenced by fuel moisture state and wind speed (van Wagner, 1987). A higher FWI indicates fire weather conditions more conducive to wildfires in environments with sufficient fuel load.
Owing to its original design for use in forest ecosystems, the FWI is especially useful for predicting the likelihood and severity of extreme events in ecosystems where weather is the primary limitation to fire (i.e. those mainly limited by moisture or temperature); its accuracy in forecasting BA in fuel-limited ecosystems is more limited (Carvalho et al., 2008; Bedia et al., 2015; Abatzoglou et al., 2018; Jones et al., 2022). Its applications encompass early warning systems, pre-suppression and suppression planning, prescribed burn planning, and effective alerting of authorities and the public to abnormal fire danger conditions. The FWI is extensively used in operational global information platforms such as the European Forest Fire Information System (EFFIS; https://forest-fire.emergency.copernicus.eu/, last access: 9 July 2024), the Global Wildfire Information System (GWIS; https://gwis.jrc.ec.europa.eu/, last access: 9 July 2024), and the Canadian Wildland Fire Information System (CWFIS; http://cwfis.cfs.nrcan.gc.ca/, last access: 9 July 2024). The FWI is not the only index for fire danger, and other fire danger systems or sub-indices of this system may correlate more strongly with BA or fire behaviour metrics in some environments. Nonetheless, the FWI is widely applied due to its good performance across a range of environments (Di Giuseppe et al., 2016; Jones et al., 2022), and so we adopt it in the current work.

In addition to well-established fire danger forecasts with lead times of a few days, skilful predictions of fire danger can be made on sub-seasonal to seasonal timescales (S2S) for Mediterranean Europe (Bedia et al., 2015), United States (Roads et al., 2010), and Asia (Spessa et al., 2015). Drought and fire weather conditions throughout the world have been found to correlate with large-scale climate patterns such as the El Niño–Southern Oscillation (ENSO) (Field et al., 2016; Chen et al., 2017) and the Indian Ocean Dipole (Cai et al., 2009) for which current numerical weather prediction systems showcase a predictive skills. Other climate modes such as the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation have been shown to influence fire-favourable weather conditions for some seasons and regions (Aragão et al., 2018; Turco et al., 2018). However, due to the larger uncertainties in their predictions, they are not considered here.

Following the concept of seamless prediction of fire weather on S2S timescales (Wetterhall and Di Giuseppe, 2018; Di Giuseppe et al., 2020, 2024; Dowdy, 2020), we collated FWI data from reanalyses and forecasts designed to operate on S2S lead times of 10 d to 7 months. Here, we take FWI estimates from the ERA5-Land reanalysis product (Muñoz-Sabater et al., 2021) as a proxy for the observed FWI. Forecasts at different lead times are taken from the operational high-resolution ECMWF weather system, and seasonal predictions are sourced from ECMWF’s seasonal forecasting system, ECMWF-SEAS5 (Johnson et al., 2019; Di Giuseppe et al., 2020, 2024). A comparison between reanalysis and forecast provides an indication of how weather fore-

cast errors translate into FWI uncertainties (predictability). Additionally, the predictions are compared to recorded peaks in fire activity, in terms of both burned areas and active fires as observed by the MODIS satellites to provide a qualitative assessment (skill) of the correlations between landscape flammability and actual fire events.

The prediction systems utilised here vary in their spatial and temporal resolutions. Short- to medium-range FWI forecasts (up to 10 d) are available daily at a resolution of 9 km with 50 ensemble members, while the FWI seasonal forecast is available monthly at a resolution of approximately 25 km: however seasonal skill is limited to 1–2 months in normal conditions (Di Giuseppe et al., 2024). All prediction systems include a measure of uncertainties through the provision of ensemble simulation (Figs. S24, S25, S26 in the Supplement). Variance across the ensemble was previously estimated to be on the order of 10%–15% (Vitolo et al., 2020).

Here, the predictive skill of the models is assessed qualitatively by visually examining the extent to which the extreme FWI (specifically the ensemble mean) was a precursor of several focal events, replicating the use of this indicator by fire agencies during the fire season. The approach is designed to partially replicate the interpretation and application of the FWI by fire management agencies. Most fire agencies would have local information on fuel conditions and would thus be able to interpret FWI values in a more informed manner, reducing the dependence of decisions on FWI anomalies alone. The FWI should not be evaluated using traditional skill scores, as these would be dominated by false alarms. We maintain that the FWI is an index representing flammability and, therefore, cannot be fairly validated against fire activities.

### 3.1.2 Identifying key drivers of focal events

#### Modelling systems

We used two modelling systems with similar fire predictors to diagnose the drivers of each focal event. The models are the PoF model (McNorton et al., 2024) and the ConFire attribution framework (Kelley et al., 2019, 2021). The PoF model diagnoses the drivers of active fire (AF) observations from the MODIS MCD14ML active fire product (collection 6.1; 1 km resolution; Giglio et al., 2016; ASA Earth Science Data Systems, 2020) using Shapley values (Lundberg and Lee, 2017), while ConFire diagnoses drivers of BA from the MODIS BA product (Giglio et al., 2018; regridded to 0.5°). Fires flagged as low confidence in the AF product were not used. Although AF and BA have been used widely in global and regional scientific studies, there are substantial differences between the two branches of fire observation as reviewed extensively elsewhere (Roy et al., 2008; Di Giuseppe et al., 2021; Chuvieco et al., 2019), and the strength of the relationship between them can vary regionally (van der Werf et al., 2017; Hantson et al., 2013). Our use of two observational
fire products and two distinct model approaches provides a way to account for inherent uncertainties in the observability of different fire events and the uncertainties in the methodologies.

Both modelling approaches use a number of individual predictors of AF or BA, which we refer to as “drivers”. The drivers are grouped into four main categories, which we refer to as the controls. PoF and ConFire both include weather, fuel abundance, and fuel moisture as controls on fire. In addition, PoF (but not ConFire) includes an “Other” category of controls, and ConFire (but not PoF) includes a “human” category of controls, as per Table 3. PoF drivers in Other include ignition and suppression effects as well as the residual error between predicted and observed fire activity. Grouping the set of drivers between the four identified controls – weather, fuel moisture, fuel load, and human/other – is not always straightforward, as fuel moisture and weather variables are strongly correlated, and fuel load is also related to weather conditions. Hence, some drivers can be associated with more than one control (Table 3). The categorisation stems mostly from the way the driving datasets have been obtained and their underlying resolutions. We have also considered the traditional approach of assessing fire weather in isolation within most fire danger assessment metrics. We believe that grouping these metrics under the umbrella term “control weather” offers a concise way to reference the drivers of the Fire Weather Index (Matthews, 2009). Despite this, it is important to note that the techniques employed ensure contribution from specific variables cannot be double-counted between categories. Both ConFire and PoF are capable of disentangling the contributions of individual drivers within the same control category (for example, the separate contributions of dead or live vegetation) and quantifying these contributions (Kelley et al., 2019; McNorton et al., 2024). However, we will focus our analysis on the impact of the controls.

## Drivers and controls used in fire event analysis

For our assessment of the contribution of weather and fuel moisture to the anomalous events, we take several predictors from ERA5-Land (9 km resolution; Muñoz-Sabater et al., 2021), specifically variables that are known to correlate with AF or BA (Bistinas et al., 2014; Haas et al., 2022). The drivers considered for each control are listed in Table 3. For the weather component in isolation, we use 2 m temperature, 2 m dewpoint temperature, 10 m wind speed, and daily total precipitation (note that these are the prerequisite variables used in the formulation of the FWI; van Wagner, 1987). We use a fuel characteristic model to estimate the fuel load and fuel moisture components following McNorton and Di Giuseppe (2024), with model estimates of fuel moisture constrained by estimates of leaf area index (LAI) from the ECMWF’s Integrated Forecast System (IFS) and model estimates of fuel loads constrained by aboveground biomass

estimates from the ESA CCI (Santoro and Cartus, 2021) and net ecosystem exchange estimates from the Copernicus Atmosphere Monitoring Service (CAMS; Agustí-Panareda et al., 2019). Additional predictors regarding fuel load and state include vegetation cover and type (Table 3). Proxies for ignition and suppression controls, placed within the “Other” set of controls, are more challenging to establish. Currently, we use population density, urban fraction, cropland fraction, pasture fraction, lightning, and orography (Table 3). For consistency all variables are interpolated to 9 km resolution. The “Other” category not only includes factors related to ignitions but also the fraction of predictions missed by the models. This is important because this category weights the importance of unaccounted-for factors.

Another important aspect is that models do not assume a specific direction for each factor’s influence on fire activity. Consequently, wetness can correlate with increased fire likelihood in some locations and reduced fire likelihood in others. This aligns with established theory in our field: in fuel-limited regions where grass and herbaceous fuels dominate, high rainfall promotes fuel accumulation and increases fire extent. Conversely, in fuel-rich regions with high tree cover, high rainfall increases fuel moisture and reduces fire extent. See Sect. S1.2.1 in the Supplement for a detailed description. See Sect. S2.1 for detailed evaluation.

## Analysis of fire drivers

The PoF system uses gradient-boosted decision trees from the XGBoost library on detected AF (McNorton et al., 2024). The training iteratively adds decision trees to an ensemble of models to correct for errors made by previous iterations, resulting in a computationally efficient optimisation (Chen and Guestrin, 2016). The system training uses a classifier approach which defines a positive hit as an AF detection within the grid cell on a given day. The driver attribution is performed using the SHapley Additive exPlanations (SHAP) method taken from the SHAP library (Lundberg and Lee, 2017). These are then combined to provide overall attribution to one of the four controls for AF predictions.

ConFire uses Bayesian inference to assess fire behaviour uncertainty by evaluating control strengths’ impact on BA. Instead of a single output, it produces a probability distribution based on data in a simplified fire model. Variables like weather and fuel moisture contribute simultaneously to the prediction of monthly BA. Monthly averages of daily values are aggregated using the Fogo Local Analisado pela Máxima Entropia (FLAME) system (Barbosa, 2024). Each control combines drivers using logistic functions. Bayesian inference optimises driver contributions and accounts for stochasticity, capturing fire unpredictability under similar conditions (Kelley et al., 2021). The mean logit-transformed BA distance with and without this term measures additional uncertainty. The model was trained, and it ran from 2014 to 2023 using driving datasets common across this period. Ini-
<table><caption>**Table 3.** Drivers of fire and their parent control group included in the event fire analyses using ConFire and PoF. Drivers are individual variables, which serve as proxies for the influence of weather, fuel load, fuel moisture, or other controls on BA. * The “Other” category includes proxies for ignition and suppression controls and the missed prediction. Note that for ConFire, explanatory variables can be associated with multiple controls (Kelley et al., 2019). Positive (+ive) or negative (−ive) under “ConFire control” describes if a driver increases or decreases BA in ConFire. NRT: near-real time.</caption><thead><tr><th>Driver</th><th>PoF control</th><th>ConFire controls</th><th>Frequency</th><th>Time period</th><th>Source</th><th>Reference</th></tr></thead><tbody><tr><td>2 m temperature</td><td>Weather</td><td>Weather +ive</td><td>Daily</td><td>Jan 2014–NRT</td><td>ERA5-Land</td><td>Muñoz-Sabater et al. (2021)</td></tr><tr><td>2 m dewpoint temperature</td><td>Weather</td><td>Weather −ive</td><td>Daily</td><td>Jan 2014–NRT</td><td>ERA5-Land</td><td>Muñoz-Sabater et al. (2021)</td></tr><tr><td>10 m wind speed+</td><td>Weather</td><td>Not used</td><td>Daily</td><td>Jan 2014–NRT</td><td>ERA5-Land</td><td>Muñoz-Sabater et al. (2021)</td></tr><tr><td>Precipitation</td><td>Weather</td><td>Weather −ive</td><td>Daily</td><td>Jan 2014–NRT</td><td>ERA5-Land</td><td>Muñoz-Sabater et al. (2021)</td></tr><tr><td>Live leaf fuel load</td><td>Fuel load</td><td>Not used</td><td>Daily</td><td>2014–2020</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Live wood fuel load</td><td>Fuel load</td><td>Not used</td><td>Daily</td><td>2014–2020</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Dead foliage fuel load</td><td>Fuel load</td><td>Not used</td><td>Daily</td><td>2014–2020</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Dead wood fuel load</td><td>Fuel load</td><td>Not used</td><td>Daily</td><td>2014–2020</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Mean and max vegetation optical depth (VOD) of the last 12 months</td><td>Not used</td><td>Fuel load +ive</td><td>Monthly</td><td>2014–NRT</td><td>Satellite – Soil Moisture and Ocean Salinity (SMOS)</td><td>Wigneron et al. (2021)</td></tr><tr><td>Vegetation optical depth (VOD)</td><td>Moisture</td><td>Moisture −ive</td><td>Monthly</td><td>2014–NRT</td><td>Satellite (SMOS)</td><td>Wigneron et al. (2021)</td></tr><tr><td>Low vegetation (LAI)</td><td>Fuel load/moisture</td><td>Not used</td><td>Monthly</td><td>2002–2020 climatology</td><td>Satellite (multi-sensor)</td><td>Boussetta et al. (2021)</td></tr><tr><td>High vegetation (LAI)</td><td>Fuel load/moisture</td><td>Not used</td><td>Monthly</td><td>2002–2020 climatology</td><td>Satellite (multi-sensor)</td><td>Boussetta et al. (2021)</td></tr><tr><td>Live fuel moisture content</td><td>Fuel moisture</td><td>Fuel moisture −ive</td><td>Daily</td><td>2014–NRT</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Dead fuel moisture content</td><td>Fuel moisture</td><td>Fuel moisture −ive</td><td>Daily</td><td>2014–NRT</td><td>Fuel model</td><td>McNorton et al. (2024)</td></tr><tr><td>Snow cover</td><td>Fuel moisture</td><td>Snow −ive</td><td>Daily</td><td>2014–NRT</td><td>ERA5-Land</td><td>Muñoz-Sabater et al. (2021)</td></tr><tr><td>Pasture</td><td>Not used</td><td>Ignitions +ive suppression −ive</td><td>Annual</td><td>2014–2023</td><td>HYDE</td><td>Klein Goldewijk et al. (2011)</td></tr><tr><td>Cropland</td><td>Not used</td><td>Ignitions +ive suppression −ive</td><td>Annual</td><td>2014–2023</td><td>HYDE</td><td>Klein Goldewijk et al. (2011)</td></tr><tr><td>Urban population</td><td>Not used</td><td>Ignitions +ive suppression −ive</td><td>Annual</td><td>2014–2023</td><td>HYDE</td><td>Klein Goldewijk et al. (2011)</td></tr><tr><td>Rural populations</td><td>Not used</td><td>Ignitions +ive suppression −ive</td><td>Annual</td><td>2014–2023</td><td>HYDE</td><td>Klein Goldewijk et al. (2011)</td></tr><tr><td>Lightning</td><td>Not used</td><td>Ignitions +ive</td><td>Monthly</td><td>2000–2020 climatology</td><td>LIS/OTD</td><td>Cecil et al. (2014)</td></tr><tr><td>Type of vegetation</td><td>Other*</td><td>Not used</td><td>Fixed</td><td>Jan 2014–NRT</td><td>ECLand</td><td>Boussetta et al. (2021)</td></tr><tr><td>Urban fraction</td><td>Other*</td><td>Not used</td><td>Fixed</td><td>Jan 2014–NRT</td><td>ECLand</td><td>McNorton et al. (2023)</td></tr><tr><td>Orography</td><td>Other*</td><td>Not used</td><td>Fixed</td><td>Jan 2014–NRT</td><td>ECLand</td><td>Boussetta et al. (2021)</td></tr></tbody></table>
tially trained on 50% of BA, it is then applied predictively to the full dataset. A separate evaluation is conducted, training on data from 2014 to 2018 and testing against 2019 to 2023, following the protocol employed by Barbosa (2024). See Sect. S1.2.2 for a detailed description and Sect. S2.2.1 for evaluation.

For both models, we include an estimate of uncertainty. ConFire is designed as an uncertainty quantification model, providing BA probabilities and their likelihoods for each region. Results in this report are based on 5%–95% confidence intervals or likelihoods, supporting central (median) estimates. ConFire uses Bayesian inference to quantify how various factors impact fire occurrence, assuming that fuel increases BA, moisture decreases it, ignitions increase it, and suppression decreases it. The model trains on historical data to determine influence levels and accounts for uncertainties from fire stochasticity and unconsidered factors. Its probability distribution is a logit zero-inflated function, assessing changes in extreme fires even with small observed areas. ConFire manages uncertainties from unpredictable weather and vegetation responses. ConFire quantifies uncertainty estimates from different drivers by constraining them with observations and addresses structural uncertainties, such as missing explanatory variables and errors in mechanistic relationships. While it represents one relationship of control to BA, the probability distribution accounts for uncertainty across various potential relationships. Noise is considered, with the stochasticity of burned area accounted for both in areas with no fire and in the probability of different potential levels of burning where fire does occur. We test ConFire’s uncertainty quantification using Bayesian inference evaluation techniques. However, ConFire does not account for some uncertainties, such as potential changes in feedbacks between fire and vegetation when transitioning to a new fire regime out of sample. The model assumes the accuracy of one bias-corrected model for fuel load (JULES-ES), neglecting uncertainty from dynamic global vegetation models (DGVMs). While BA anomalies are used to reduce observational bias, any disagreements in bias across space or time are not included in ConFire’s training.

Meanwhile, PoF outputs are provided in terms of probabilities calculated using ensemble predictions from weather forecasts each to generate a set of binary classifiers. The probabilities are therefore based on a wide parameter space, taking into account uncertainties in both the input parameters and the stochasticity of the classification algorithm itself.

## 3.2 Results

### 3.2.1 Predictability of focal events using fire weather forecasts

#### Canada

The early establishment of fire weather conditions as well as the late cessation of the fire season is well captured in the

FWI reanalysis in Canada (Fig. 7). The FWI also captures the intermittent pattern of fire danger and its correlation to actual fire activity. However, at the seasonal timescale, the signal is weakened, and there were no prior indications that the upcoming season would have been as extreme as it was with respect to fire activity (Fig. 7). For the most part of 2023, Canada was in drought. The weather-limited nature of the Canadian fire season means that the FWI modelling framework serves as an essential indicator of anomalous conditions, acting as a prerequisite for the intensity and spread of fires. It provides valuable insights into the sequence and extent of extreme fire weather days during such events. Notably, in this region, peaks of fire activity correspond to peaks of landscape flammability, and there is a good correlation between observed fire activity and predicted fire danger.

#### Greece

The establishment of fire-prone conditions in the Mediterranean, particularly in Greece, is part of the region’s seasonal weather cycle (Fig. 7). In 2023, this pattern persisted, and extreme landscape flammability could be forecasted well in advance. In arid and semi-arid regions, fire occurrence is driven not solely by weather but also by fuel availability and its intermittent short-term drying. In these regions, the FWI often reaches extreme levels for much of the summer. However, fires do not always occur even when the FWI is extreme, as ignition and early suppression play a crucial role. The anomalous fire extent in Alexandroupolis, including the large Evros fire, highlights the limitations of relying solely on fire weather indices in these areas. There were no discernible indications in the FWI records that the particular day would be more extreme than the days before or afterwards, emphasising the need for a more holistic approach to fire risk assessment in regions where fuel is a limiting factor or live fuel moisture plays a crucial role in the extent of the fires (Di Giuseppe, 2023).

#### Western Amazonia

The correlation between the FWI and fire activity in the western Amazonia region at the shorter lead times is generally poor, primarily due to the strong dependency on either lightning or human ignitions (Kelley et al., 2021). In 2023, this pattern persisted (Fig. 7), with the onset of fire weather following the seasonal pattern well ahead of the time where fires were triggered. Seasonal predictions indicated high fire danger during the summer period, probably driven by El Niño conditions.

### 3.2.2 Identifying key drivers of focal events

Weather, fuel moisture, fuel abundance, and ignitions are the four primary controls identified as influencing the occurrence and intensity of the focal fire events. Anomalies in individual
Fire Activity & Prediction 2023

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

**Figure 7.** Chicklet plots displaying seamless FWI predictions over time from various forecasting systems of the ECMWF (see Methods). The x axis corresponds to specific dates throughout the year, while the y axis denotes either observations or the time leading up to the date when a forecast was generated. The vertical colour coherence allows for quick identification of the time windows of predictability associated with the observed fire activity provided in terms of both the number of detected active fires in a day and the total burned area in a month (circles).
drivers of these controls, such as temperature or soil moisture, are calculated by comparing regional daily 2023 averages with the average for 2003–2022. Dead fuel, with its lower moisture content and higher combustibility, often plays a significant role in determining fire ignition. During extreme events, it is the dry live fuel that burns, contributing to the overall severity and intensity of the fire.

Analysing the time series of key drivers contextualises the conditions under which events occurred (see Fig. 8). However, leveraging the PoF and ConFire models allows for a statistical causality attribution of the four controls for observed fire occurrence (see Fig. 9). These models will provide control attribution even if no fire event is recorded, with a low probability across all controls indicating an accurate prediction. High fire probability without recorded fire activity could indicate successful suppression or fire-prevention policies. Unaccounted human influence is categorised under “Other”, encompassing variables not forecasted by the models. This analysis enhances our understanding of fire activity controls and helps identify missing information that degrades the quality of the prediction.

### 3.2.3 Drivers of active fire extremes

#### Canada

Persistent fire-favourable weather conditions played a crucial role in controlling the extent of active fires in Canada during the summer of 2023. Dry weather contributed to extensive drying of both live and dead vegetation, further exacerbating fire risk (Figs. 8, 13). Most of the explainability of the Canada event comes from anomalous weather conditions. Increased lightning activity often coincides with or precedes significant fire periods, indicating lightning as a key source of ignitions in the region given the contribution of the cloud-to-ground flashes to the total predicted lightning activity. This is in agreement with the attribution of 59% of wildfires and 93% of total BA to lightning ignition sources in Canada during 2023 (Jain et al., 2024). Adverse weather conditions in mid-May in western Canada were identified as influential factors in shaping fire events. However, multiple instances of intense burning events, notably in mid-July, early August, and late September, fall into the “Other” category, heavily contributing to the total number of events for which there is no attribution among the controls. The fact that clusters of events were not predicted suggests potential inadequacies in accounting for some ignition sources or accurately representing fire propagation across these landscapes.

#### Greece

The driver anomalies (Fig. 8) and control attribution (Fig. 9) did not suggest an abnormally fire-prone year in Greece, failing to explain the large fire extent around the time of the Evros fire near Alexandroupolis. An anomalously wet spring may have led to increased foliage and subsequently quick

drying of plant material. A sustained dry period in late July and August further dried out new foliage, creating favourable conditions for fire activity, as indicated by the anomalously dry live and dead fuel moisture content in August. Despite these conditions, the unexpected extent and severity of fires around Alexandroupolis were not predicted, highlighting the intrinsic difficulties in forecasting isolated extreme events even when most predictors are included. Additionally, the high wind speeds at the time partially contributed to the extensive BA during the fire.

#### Western Amazonia

Prolonged drought conditions driven by a positive ENSO (Aragão et al., 2007; Jiménez-Muñoz et al., 2016), stemming from anomalously low rainfall and high temperatures, created favourable conditions for an active fire season in western Amazonia (Figs. 8, 9). These conditions had a significant impact on the typically wet ecosystem, affecting soil moisture as well as live and dead fuel moisture. Despite weather conditions serving as a persistent control for fire activity, several intense active fire periods in late August and throughout September were not predicted, possibly due to unrepresented ignition sources. Additionally, fire activity from September onwards was intensified by intense lightning activity, characteristic of the region, which substantially contributed to ignitions.

### 3.2.4 Drivers of regional burned area extremes

#### Canada

ConFire detected a significant anomaly in BA starting in late April, as seen in the observations (Fig. S9) with very high confidence between May (99.2% likelihood) and June 2023 (> 99.9% likelihood; Fig. 10). While less confident in a positive anomaly in September and August (71% likelihood), ConFire detects the possibility of much higher burning in August and September, corresponding with the increase in burning in the western Taiga Shield (Fig. S9). Figure 10 shows the controls that contribute to these anomalies. Our analysis indicates a > 99.9% likelihood that elevated fire weather conditions persisting throughout the 2023 fire season led to a notable increase in burning, explaining 19 [4.6–45] % of the BA anomaly in May and 13 [1–110] % in June (median estimates, with 5th–95th percentile range in square brackets). Anomalous weather conditions subsided in May through the early summer, though by September there was an increased likelihood of contributing to the increase in BA anomaly seen in the late fire season. Drier fuel conditions could have contributed significantly to the increase in BA (up to 65% of the BA anomaly in May and 45% in June), though with low confidence (60.5% in May and 61.3% in June), and wetter fuels exerting a suppressive effect on fire spread was also possible, suggesting their potential role in mitigating fire severity. A small but confident suppressive effect (100% likelihood
<figure><img src="image_12.png" /><figcaption>**Figure 8.** Anomaly driver stripes for the three focal events. The drivers are selected to contextualise the conditions under which the examined events took place. All values are expressed as anomalies compared to the 2003–2021 climatology with the exception of lighting activity, which is expressed as absolute flash density.</figcaption></figure>

in May, 64.8% likelihood in June) from fuel load was observed, reducing relative increases in BA by 1.4 [0.17–7.1] % in May. Direct human-induced landscape changes exhibited a small impact on the extent of burned areas (likelihood of 97.4% in May), explaining between 5.4 [1.2–22] % of the anomaly in BA in May and 5.2 [0.6–24] % in June.

### Greece

The analysis reveals an anomalously high BA, particularly from mid-August onwards, though with a lower confidence level compared to the Canadian case (69.9% likelihood; Fig. 10). Figure 10 shows the controls that contribute to these anomalies. With very high confidence, our findings demon-
Fire controls 2023

<figure><img src="image_13.png" /><figcaption>**Figure 9.** Contributions of different fire controls to daily active fire anomalies in our focal events. All values are scaled to the observed daily fire anomaly such that the sum of the four daily control values matches the total observed anomaly (see Table S1).</figcaption></figure>
strate the presence of anomalously high fire weather conditions during the 2023 fire season in Greece (98.3% in July and > 99.9% in August and September). In August, these conditions explained 24 [4.3–140]% of the increased BA, increasing to 34 [5.6–170]% by September. Assessing the impact of fuel moisture on BA, our analysis shows a wide range of possibilities, with confidence ranging from a 21% increase to a 180% decrease in relative BA extent, which would have offset some of the increases from fire weather. This uncertainty underscores the complexity of the interactions between fuel moisture and fire behaviour in Greece. Although direct human-induced landscape changes exerted greater influence on BA extent in Greece than in the other focal regions, this influence remained small compared with weather factors. The analysis indicates a slightly higher-than-normal fuel load, with only a low likelihood of having a substantial influence on increased levels of burning.

## Western Amazonia

Our analysis indicates a reasonably high confidence that the considered drivers contributed to anomalous high burning during September (73.8% likelihood), October (94.9% likelihood), and November (96.1% likelihood; Fig. 10). Figure 10 shows the controls that contribute to these anomalies. The primary driver of the observed BA anomaly appears to be dry fuel conditions, with a very high likelihood (99.7%) of drier-than-normal conditions persisting through November. This led to a substantial increase in BA, explaining at least 57% of the increase in BA. While fire weather conditions were also elevated (likelihood > 99.9%), their impact on BA was comparatively lower, resulting in at least 2% increase in BA during October and November, though with a small probability of contributing much more. Direct human influence was identified as a contributing factor, with a high likelihood (92.9% in September, 94.5% in October) of increasing burned area. However, the magnitude of this influence was approximately one-tenth of that attributed to fuel dryness. There is little confidence in the direction of the effect on BA anomaly, with potential influences ranging from a slight suppressive effect (26.5% likelihood) to potentially explaining the majority of the increased BA in September to virtually no impact in October. This suggests that fuel dynamics played a minor role in driving the observed fire activity. The analysis reveals a higher confidence in the simulations indicating positive anomalies, indicating a robust signal in the attribution of drivers to observed BA anomalies.

### 3.2.5 Spatial variation in drivers of burned area extremes

#### Canada

In Canada, most BA anomalies were linked to widespread high fire weather, with 95% of the country being influenced by higher-than-normal fire weather (Fig. S12). There was

a tendency for fuels to dry out, although this was not as widespread. Fuel load anomalies were more scattered, but areas of low fuel anomaly did correspond to some boundaries in fire extremes (Figs. S11, S12). Increased human influence may have had some influence at suppressing fires, but this is not significant, and in some places, the model indicates a small possibility of increased fire from human activity. In the eastern Taiga Shield, fire extremes in some areas were driven by high fire weather and dry fuel, compounded with more vegetation cover and hence higher-than-normal fuel load in some places (Fig. 11). However, the borders of extreme fires corresponded to a suppressive effect from decreased fuel load. In the western Taiga Shield, dry fuel and high fire weather drove fire incidents, with high fire weather dominating in some areas. Increased suppression may have had some influence at suppressing fires, but this is not significant, and in some places, the model indicates a small possibility of increased fire from human activity.

In June, there were high anomalous burned areas in Quebec's eastern Taiga Shield, which were divided into two major fire components with a slightly reduced BA in between (Fig. 11). Both components were associated with high fire weather, but in areas where high fire weather occurred without the contribution of other controls, it tended not to cause high levels of burning. The highest burned areas were mainly found in the northern component and were associated with anomalously low levels of moisture and high fire weather. Some cells with the very highest BA also showed anomalously high fuel load (Fig. 11). In a region further north, around 56–57°N, 72–80°W, there was high fuel load, dry conditions, and high fire weather, but fire in the area was found to be highly fuel-limited and largely insensitive to even large changes in controls (Kelley et al., 2019). The southern component of high burning corresponded with high fire weather and either fire fuel or high fuel load. Additionally, any boundaries between higher and normal/lower levels of BA also saw lower-than-average fuel loads, which may have inhibited fire spread (Fig. 11).

In May, the western Taiga Shield, Taiga Plains, and boreal plains experienced higher-than-normal fire weather across the region (Fig. S13). The increased burned areas in the west were due to extreme low fuel moisture and high fire weather, as well as higher fuel loads. In contrast, areas to the south experienced high fire weather without the extreme burned areas. Additionally, anomalies in fuel loads and burning levels became more evident in September, with some areas displaying lower-than-average fuel and burning (Fig. S13). These anomalies persisted, with regions still experiencing high fire weather and variations in fuel moisture levels. Furthermore, the eastern areas with higher fire weather also showed higher fuel loads, while drier fuel moisture was observed in less extreme regions to the east. Additionally, specific locations saw higher fire weather and above-average fuel moisture, while areas just north of the extreme fires experienced wetter-than-normal fuel moisture (Fig. S13).
<figure><img src="image_14.png" /><figcaption>**Figure 10.** The influence of fuel load, fuel dryness, fire weather, and human controls on burned area (BA) anomalies in 2023 for each focal region (a–c). In each month, the first column shows the BA anomaly, with the colour indicating how well each model iteration captures variations in BA (see legend “Variations in burned area explained by the model”). The green, yellow, pink, and blue columns show the percentage of the anomaly explained by fuel load, fuel dryness, fire weather, and human influence, respectively, across the full distribution of model iterations. See also Table S3 in the Supplement.</figcaption></figure>

Greece

Interestingly, most of Greece showed a tendency towards less suppression from people (Fig. S12). However, the dominant driver over most (73%) of Greece was high fire weather, with some areas in central Greece showing notably low fire weather. These areas do not correspond to a fire anomaly, though higher fuel loads were detected. Except for central Greece, other areas of lower-than-average BA correspond to lower-than-average fuel loads (Fig. 12). There was no significant anomaly in fuel moisture across Greece, though the northeastern fire extreme does correspond to a joint increase in fire weather and decrease in fuel moisture. Extremes in eastern coastal Greece correspond to anomalies in fuel load, fuel dryness, and heightened fire weather (Fig. 12).

In August, northern Greece experienced high fire weather and low fuel moisture, particularly around Alexandroupolis

in Macedonia and Thrace (Fig. 12). The region extended further east, experiencing extreme fires that reached into central Macedonia. Unlike Canada, the framework in north Greece did not show the same level of detail in the boundaries around extreme levels of burning. However, the transition to less burning in the south of west Macedonia did correspond to reduced fuel load. Areas around Athens and central Greece that saw unusually high levels of burning also experienced decreased fuel moisture and increased fuel load (Fig. 12). In contrast, areas in southern Thessaly and central Greece that did not experience highly burned areas saw lower-than-normal fire weather. In the Peloponnese region, there was either high fire weather or reduced fuel moisture, but these conditions rarely occurred together, which might explain the lack of increased BA throughout the region (Fig. 12).
<figure><img src="image_15.png" /></figure>

**Figure 11.** Anomalies in controls during months and regions of high BA in Quebec. The top map of each region shows BA anomalies at 0.5° for that month in 2023 versus the 2014–2023 monthly average. The middle maps look at anomalies in controls that would cause higher BA, with areas not greyed out representing regions with greater than monthly average BA in 2023. The bottom map shows drivers that would have led to lower than normal levels of burning, with areas not greyed out showing lower or non-change from monthly average BA in 2023. Each grid cell has four points: green points show anomalies in fuel load, purple in fuel moisture, red in fire weather, and cyan in humans. This way, we can see if controls acted in unison to cause extreme levels of burning or prevent extreme fires from extending further. The shade of the point shows the most likely expected level of anomaly in that control, while the size shows how confident we are in the direction of the anomaly.
<figure><img src="image_16.png" /><figcaption>Figure 12. Same as Fig. 11 but for Greece.</figcaption></figure>

## Western Amazonia

High fire weather anomalies were almost universal across our western Amazonia region (Fig. S12). However, anomalies in other controls varied widely across the region, which appears to modulate the occurrence of areas with above-average BA (Figs. S12, S14). In September, the regions of high burning in the western Amazon all exhibited higher-than-average fire weather, with the highest BA anomalies associated with the highest fire weathers. The areas with the highest BA anomalies along the Amazon River were located around Manaus and also showed lower-than-average fuel moisture and higher-than-average fuel load. The very highest pixels exhibited the anomaly in increased fuel loads. In the southern and central part of the region (65 to 62° west, 7/8° south), near Porto Velho, BA anomalies were associated with extremely high fire weather and higher fuel loads. The areas of highest burning also showed an increase in human-driven burning. Further east (59° west, 7° south), the highest fire weather and decreased fuel moisture occurred alongside higher burning. Areas of higher BA in the north (63° west, 0° north) were associated with extreme fire weather, though they were offset by below-average fuel loads. This may explain why they were not as extreme as the fires around Manaus, though the area is not generally considered fuel-limited (Kelley et al., 2019), and fuel had little overall impact across the region (Fig. 10).

# 4 Attribution to global change factors

## 4.1 Methods

Many of the direct drivers and controls on fire events, outlined in the Sect. 3 (e.g. weather, fuel, moisture, ignition and suppression), are influenced by global change factors such as climate and land-use change. Since the pre-industrial era, global mean temperature has increased by between 1.1–1.3 °C (Betts et al., 2023; Forster et al., 2024), with greater rates of warming at higher latitudes, adding potential for fuel drying. Climate change has also resulted in altered precipitation patterns, with total rainfall and dry season length increasing or decreasing variably across regions (Polade et al., 2014; Swain et al., 2018; IPCC, 2023a). Meanwhile, changes to fuel load and ignition rates are driven by climate change and anthropogenic land use, with varying effects regionally (Finney et al., 2018; Romps, 2019). For example, in fuel-limited savannah biomes, land-use change can drive more fragmented fuel loads and a reduction in fire (or an increase in fire resulting from land abandonment), whereas in forest ecosystems, fragmentation provides more potential for ignition and leads to increases in fire occurrence (Andela et al., 2017; Rosan et al., 2022).
### 4.1.1 Overview of attribution approaches

In this report, we apply various modelling techniques for each focal region to attribute (i) regional changes in the probability of high fire weather to anthropogenic forcing (Sect. 4.1.2) and (ii) changes in monthly BA to total climate forcing, socio-economic change, and all forcing (Sect. 4.1.3).

The types of forcing considered vary across the attribution techniques applied, and so here we define the terminology used throughout the paper when describing attribution results (summarised in Table 4).

Our attribution to anthropogenic forcing explicitly targets the changes driven by anthropogenic greenhouse gas emissions and land-use change, following the IPCC WGI definition (Hegerl et al., 2009; Mengel et al., 2021). We prescribe these emissions in a model to specifically isolate human forcing from natural variability (Sect. 4.1.2).

Our attribution to total climate forcing considers changes driven by climate change since the pre-industrial period, including both anthropogenic forcing and natural variability in line with the IPCC WGII and the Inter-Sectoral Impact Model Intercomparison Project 3a (ISIMIP3a) definition of climate change impact attribution (IPCC, 2023b, c; Mengel et al., 2021). This involves comparing simulations driven with historical reanalysis to a detrended counterfactual simulation with the historical warming signal removed (with both simulations including historical transient land-use change), and therefore only the impacts of climate change are attributed, not distinguishing between anthropogenic or natural causes (Mengel et al., 2021; Burton et al., 2024).

Our attribution to socio-economic factors is applied via the same set of simulations as our attribution to total climate forcing. The role of socio-economic factors is isolated by comparing the early industrial period to the late industrial period in the counterfactual scenario, in which only land use and population density are allowed to change (Burton et al., 2024).

Finally, attribution to all forcing compares the early industrial period in the counterfactual scenario to the last industrial period in the factual scenario, which gives the net effect of all forcings combined. These are summarised in the Table 4 below.

The tools described here enable us to assess the influence of climate and socio-economic forcing on fire with respect to three different target variables. We use the FWI to assess how the probability of high (90th percentile) fire danger has changed as a result of anthropogenic forcing. As climate change has a direct impact on fire weather, this approach enables us to isolate its effects without confounding factors of land-use change and ignitions and reveals how a fire might develop once ignited.

In a second branch of analyses, we attribute the change in BA to total climate forcing, socio-economic factors, and all forcing, specifically targeting the observed month of peak burning in the 2023–2024 fire season for each focal event.

We use ConFire to assess the change in likelihood of a BA fraction (BA divided by the total area available to burn) that lies in the 90th or 95th percentiles of observations from the 2023–2024 fire season (percentile thresholds vary regionally due to differing domain sizes).

Separately, we attribute change in the monthly median BA in the present day using simulations from fire-enabled dynamic global vegetation models (DGVMs) contributing to the Fire Model Intercomparison Project (FireMIP). Each of these methods is described in more detail below.

In each approach we include an explicit estimate of uncertainty. We use bootstrapping to give uncertainty estimates for the FWI risk ratios. ConFire is designed as an uncertainty quantification model (see Sect. 3.2.4), giving the likelihood of all possible burned areas for each region based on a probabilistic analysis of past burn patterns and environmental conditions. We combine the information from the FireMIP models in a weighted multi-model ensemble to give uncertainty ranges across the models. Each result therefore presents a 5–95th percentile probability estimate.

### 4.1.2 Attributing change in likelihood of fire weather to anthropogenic forcing

We use an established approach to attribute change in probability of high (90th percentile) fire weather conditions to anthropogenic forcing. The approach uses estimates of the FWI, as used in previous studies from the World Weather Attribution (Barnes et al., 2023), using outputs from the HadGEM3-A large ensemble (Christidis et al., 2013; Ciavarella et al., 2018). It follows the approach introduced by Stott et al. (2004) for attributing extreme weather events, and it has been employed in other attribution studies targeting fire weather, such as S. Li et al. (2021).

As outlined in Sect. 3.1.1, the FWI is used operationally and in research contexts to rate fire danger based on meteorological conditions. Due to the availability of model output variables, we use maximum daily temperature at 1.5 m as a proxy for noon values, total daily precipitation, mean daily relative humidity at 1.5 m, and mean daily wind speed at 10 m, following Perry et al. (2022). We calculate the daily FWI for the month of 2023–2024 peak BA anomaly for each focus region, using the same month and region for validation over the historical time series (1960–2013).

We validate and bias-adjust the model estimates of the high FWI for the period 1960–2013 by comparing a 15-member HadGEM3-A ensemble with ERA5 reanalysis data (C3S, 2024) representing the “observed” FWI. The 0.25° resolution observed FWI from ERA5 was coarsened by linear interpolation (calculated by extending the gradient of the closest two points) to match the 0.5° model grid. We compare the time series of individual components of the FWI (Fig. S40) and the distribution of the modelled and observed FWI (Supplement Figs. S41–S43) and apply a simple linear regression to find the bias correction required for the 2023
<table><caption>**Table 4.** Summary of the attribution approaches used in this report.</caption><thead><tr><th>Term</th><th>Definition</th><th>Experiments compared</th><th>Framework</th><th>Application</th></tr></thead><tbody><tr><td colspan="5">Event attribution for fire weather</td></tr><tr><td>Anthropogenic forcing</td><td>Change in fire weather driven by anthropogenic emissions from greenhouse gases, land-use change and aerosols. As per Ciavarella et al. (2018) and S. Li et al. (2021).</td><td>ALL: natural forcing and human emissions<br>NAT: natural-only forcing from solar variability and volcanoes</td><td>HadGEM3-A attribution ensemble. 0.5° resolution.</td><td>Fire weather (FWI)</td></tr><tr><td colspan="5">Impact attribution for burned area</td></tr><tr><td>Total climate forcing</td><td>Changes in BA due to climate change, irrespective of the cause of warming. As per ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) (Mengel et al., 2021, and Friel er et al., 2024).</td><td>Factual (2003–2019): present-day climate (driven by GSWP3-W5E5 reanalysis), CO<sub>2</sub>, land use and population.<br>Counterfactual (2003–2019): de-trended historical climate (warming signal removed), CO<sub>2</sub> fixed at 1901 value, present-day land use and population.</td><td>ISIMIP3a impact attribution. 0.5° resolution.</td><td>FireMIP ensemble and ConFire</td></tr><tr><td>Socio-economic factors</td><td>Changes in BA due to land-use and population change. As per Burton et al. (2024).</td><td>Counterfactual (1901–1917): warming trend removed, fixed 1901 CO<sub>2</sub>, limited land-use and population change.<br>Counterfactual (2003–2019): warming trend removed, fixed 1901 CO<sub>2</sub>, present-day land use and population.</td><td>ISIMIP3a impact attribution.</td><td>FireMIP ensemble and ConFire</td></tr><tr><td>All forcing</td><td>Changes in BA due to climate, land-use, and population change. As per Burton et al. (2024).</td><td>Counterfactual (1901–1917): warming trend removed, fixed 1901 CO<sub>2</sub>, limited land-use and population change.<br>Factual (2003–2019): historical climate driven by reanalysis</td><td>ISIMIP3a impact attribution.</td><td>FireMIP ensemble</td></tr></tbody></table>

model output. See Supplement Sect. S1.2.3 for detail. Before bias adjustment, the modelled FWI is generally higher than the observed FWI, and some regions (e.g. Greece) require a larger correction than others. The correction adjusts the trend and absolute value while maintaining variability, and the model successfully reproduces the observed distribution after applying the correction in each region (see Supplement Sect. S2.3 for full evaluation).

For the events occurring in the 2023–2024 fire season, we calculate the FWI from the HadGEM3-A model simulations comprising two experiments of 525 members each, one driven by all forcings including historical greenhouse gas emissions, aerosols, zonal-mean ozone concentrations, land-use change and natural forcing (ALL), and a second counterfactual simulation with natural-only forcing from solar variability and volcanic emissions and 1850 land use (NAT) (see Table 4). By applying the bias adjustment from the previous step and comparing the fire weather in the two simulations to the 2023-observed FWI from ERA5, we calculate the change in probability of high (90th percentile) fire weather due to anthropogenic forcing.

### 4.1.3 Attributing change in regional burned area to total climate forcing, socio-economic factors, and all forcing

#### Peaks in burned area during 2023–2024

We use the ConFire attribution framework to attribute anomalies in BA fraction in the month of peak burning during the 2023–2024 fire season to total climate forcing and socio-economic factors using the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) 3a attribution protocol (see Table 4). For Canada and western Amazonia, the attribution approaches are applied to cells with BA fractions in the 95th percentile of the BA fraction distribution during 2023–2024. For Greece, the attribution approaches are applied to cells with BA fractions in the 90th percentile of the BA fraction distribution during 2023–2024, with the lower percentile threshold selected due to the smaller domain size of Greece.

We trained ConFire on observed monthly BA from the MODIS BA product during 2003–2019 at 0.5° across the entire region. For model training, we drive ConFire with Global Soil Wetness Project Phase 3 (GSWP3-W5E5) forcings provided at a 0.5° spatial resolution by ISIMIP3a (Table 5). The land surface information (tree cover and non-tree veg-
etated cover) is derived from the JULES-ES ISIMIP configuration (Mathison et al., 2023) driven by GSWP3-W5E5. This model includes dynamic vegetation, i.e. changing vegetation cover in response to climate variables, growth, plant competition, and mortality. So as not to double-count the impact of fire, we turn the interactive vegetation-fire model off. The bias in this land surface information is adjusted to the MODIS Vegetation Continuous Fields collection 6.1 remotely sensed data for <60°N (DiMiceli et al., 2022) and collection 6 for > 60°N (DiMiceli et al., 2015) using a trend-preserving empirical quantile mapping bias adjustment method. This method significantly reduces the model bias in the JULES-ES output for most regions and variables, ensuring accurate means and distribution while preserving trends between historical and future periods (Fig. S22). See Supplement Sect S1.1.1 for details.

We ran ConFire in predictive mode on monthly time steps with a structure similar to that used in Sect. 3.1.2, again grouping specific drivers into controls (Table 5). However, specific driving variables differed for this application: fuel load controls were represented by total vegetation cover and tree cover; fuel moisture controls were represented by mean consecutive dry days within each month, the fraction of dry days within the month, daily mean precipitation, mean and maximum monthly temperature, and mean and maximum vapour pressure deficit (VPD); ignition controls were represented by climatological lightning, pasture, crop, and population density; and suppression controls were represented by pasture, crop, and population density. ConFire’s Bayesian inference procedure proved useful because it allowed us to discern individual driver contributions (Gelman et al., 2013; Kelley et al., 2023). These modified ConFire simulations applied to all data in each of three experiments (see “Simulation framework” in the Supplement).

To determine the impact of total climate forcing and socioeconomic factors on the increased BA during our focal events, we conducted a paired sampling of monthly BA in the target months (see Table 4). As there is no climate influence in the early industrial simulation, we first adjusted the target event (a monthly regional BA value) to that expected without climate change. For this adjustment, we find the percentile of the observed BA in the factual scenario and find the BA at the same percentile in the counterfactual scenario. We used paired samples to account for the uncertainty in the underlying mechanisms relating our drivers to BA, which would co-vary between experiments as per Kelley et al. (2021). In total, we took 200 samples over the 17 years of each simulation, resulting in 3400 pairs.

The likelihood was then simply determined by the number of ensemble members in the factual scenario that predicted greater BA than the counterfactual scenario for total climate forcing or the counterfactual scenario predicting greater BA than the early industrial scenario for socioeconomic factors. The relative increase in BA extent is the BA in factual over

counterfactual (total climate forcing) or counterfactual over early industrial (socioeconomic).

As per Sect. 3.1.2, we evaluated the model following Barbosa (2024). We separately train ConFire on 50% of the data between 2003–2011 and perform evaluation for the years 2012–2019. Further details of the model fitting and validation can be found in the Supplement Sect. S2.2.2.

## Background changes in burned area during 2003-2019

Finally, we attribute changes in median monthly BA across all months in 2003–2019 to total climate forcing, socioeconomic factors, and all forcings, using the novel attribution method developed using state-of-the-art global fire models from the FireMIP (Burton et al., 2024). This represents an assessment of how BA has changed during the 2003–2019 period versus counterfactual scenarios. Our method employs the same ISIMIP3a simulation framework outlined above with seven fire-enabled DGVMs (see Table S3 in the Supplement) for the period 1901–2019 for the factual and counterfactual experiments (see Table 4 for descriptions). ConFire was not used in this element of our attribution approaches; rather, the native fire modelling scheme of each fire-enabled DGVM was employed. Model fire schemes are described in Burton et al. (2024).

A weighted ensemble of the monthly outputs of BA, based on the regional performance of the unweighted models against observational data from GFED5 and FireCCI, is used for the analysis. Due to large differences in absolute values of BA between the GFED5 and FireCCI observational datasets and across the models, the weightings in the ensemble are based on model capability to capture relative anomalies present in the observational datasets on a regional basis, and all changes are reported as relative anomalies. We focus on the change in median monthly BA across all months in 2003–2019 because the fire models underpredict the high tails of the distribution. The weighted models are randomly resampled to generate uncertainty estimates for each region. The method and results are reported in full for all 43 IPCC AR6 regions in Burton et al. (2024), and in the current report we select the IPCC regions that align most closely with our focus regions defined in Sect. 3.2.
<table><caption>**Table 5.** Explanatory variables used for attributing extreme BA (Sect. 4.1.3) and for the multidecadal outlook (Sect. 5.1.2). The explanatory variables are forcing data from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocols, ISIMIP3a and ISIMIP3b (Frieler et al., 2024). Positive (+ive) or negative (−ive) under “Controls” describes if a driver increases or decreases BA.</caption><thead><tr><th>Variable</th><th>Controls</th><th>Construction</th><th>Source</th><th>Reference</th></tr></thead><tbody><tr><td>Max. consecutive dry days</td><td>Moisture +ive</td><td>Monthly max of running count of days since rainfall > 0.1 mm m<sup>-1</sup></td><td>Based on precipitation from ISIMIP3a/3b</td><td>Frieler et al. (2024)</td></tr><tr><td>No. dry days</td><td>Moisture +ive</td><td>Fractional no. days of rainfall < 0.1 mm m<sup>-1</sup></td><td>Based on precipitation from ISIMIP3a/3b</td><td>Frieler et al. (2024)</td></tr><tr><td>Maximum monthly temperature</td><td>Moisture +ive</td><td>Maximum of maximum daily temperature within the month</td><td>Daily temperature approximated as ISIMIP3a/3b daily mean temperature + 0.5 × daily temperature range</td><td>Frieler et al. (2024)</td></tr><tr><td>Mean monthly temperature</td><td>Moisture +ive</td><td></td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024)</td></tr><tr><td>Mean monthly vapour pressure deficit (VPD)</td><td>Moisture +ive</td><td>Mean of daily values constructed from specific humidity, surface pressure, and max. temperature</td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024); Barbosa (2024)</td></tr><tr><td>Maximum monthly VPD</td><td>Moisture +ive</td><td>Max of daily values</td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024); Barbosa (2024)</td></tr><tr><td>Tree cover</td><td>Moisture −ive and fuel +ive</td><td>JULES-ISIMIP annual mean tree cover bias-corrected to VCF vs JULES-ISIMIP3a factual interpolated to monthly from annual values</td><td>Joint UK Land Environment Simulator Earth System model (JULES-ES)-ISIMIP corrected to MODIS Vegetation Continuous Fields (VCF)</td><td>DiMiceli et al. (2015); Adzhar et al. (2022); Mathison et al. (2023)</td></tr><tr><td>Total vegetation cover</td><td>Fuel +ive</td><td>Tree cover plus non-tree vegetated cover simulated by JULES and bias-corrected as above</td><td>JULES-ES-ISIMIP corrected to MODIS VCF</td><td>DiMiceli et al. (2015); Adzhar et al. (2022); Mathison et al. (2023)</td></tr><tr><td>Lightning</td><td>Ignitions +ive</td><td>Climatology</td><td>LIS taken from ISIMIP3a</td><td>Kelley et al. (2014); Frieler et al. (2024)</td></tr><tr><td>Cropland</td><td>Ignitions +ive, suppression −ive</td><td>Interpolated from annual to monthly</td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024))</td></tr><tr><td>Pasture</td><td>Ignitions +ive, suppression −ive</td><td>Interpolated from annual to monthly</td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024)</td></tr><tr><td>Population density</td><td>Ignitions +ive, suppression −ive</td><td>Interpolated from annual to monthly</td><td>ISIMIP3a/3b</td><td>Frieler et al. (2024)</td></tr></tbody></table>
## 4.2 Results

### 4.2.1 Change in the likelihood of high fire weather in 2023–2024

#### Canada

The fire weather conditions in Canada during June 2023 were 2.9–3.6 times more likely due to anthropogenic forcing. Here we assess the 95th percentile of the FWI over the country during the month of peak anomaly in BA (June) in the ALL and NAT HadGEM3 simulations. More of the ALL distribution lies above the observed 95th percentile of the FWI from ERA5 compared to the NAT distribution (Fig. 13), and we therefore conclude that the probability of experiencing the high fire weather observed during June 2023 is more likely in a climate forced with anthropogenic emissions.

#### Greece

The high fire weather conditions experienced during the peak anomaly in BA in August 2023 were 1.9–4.1 times more likely due to anthropogenic forcing (Fig. 13). In this case the 95th percentile of the FWI is outside of the distribution, so instead we assess the 90th percentile of the FWI over the country. This is likely a result of our linear inference of 2023 for the bias correction based on the 1960–2013 period, where in fact 2023 was so anomalous that it does not fit this trend. The 2023 event threshold here also lies at the very high end of simulated fire weather, meaning it was very unusual in the model simulations. The result range here is also larger than for Canada, meaning there is less certainty about how much human influence has increased the probability, although it does highlight at least a 50% increase in likelihood of high fire weather.

#### Western Amazonia

High fire weather in western Amazonia during September–October 2023 was 20.0–28.5 times more likely due to anthropogenic forcing (Fig. 13). In this region there is a large shift in the ALL forcing distribution compared to the NAT only forcing for the 95th percentile of the FWI, and the high risk ratio shows a strong anthropogenic signal in driving the meteorological conditions that led to high fires over this period.

### 4.2.2 Change in the likelihood of peaks in burned area in 2023–2024

#### Canada

We show that total climate forcing was virtually certain (99.9 % confidence) to have led to greater BA in Canada during June 2023 (Fig. 14). Our attribution results indicate that total climate forcing increased BA extent by 4.1 [2.3–7.8] %

during the month of June across the period 2003–2019. Additionally, considering the anomalies in fire drivers during 2023, we estimate an additional 5.7 [1–30] % absolute increase in BA extent during June 2023, on top of the 2003–2019 period (Fig. 10). The impact of socio-economic factors is less certain, with only a 64.8 % likelihood of decreasing burning, affecting BA extent by between –22.5 % and 6.6 % during 2003–2019.

Overall, we estimate that BA in Canada in June 2023 was 10.1 [3.3–40.1] % greater due to total climate forcing in the 2003–2019 period combined with this year's anomaly in the climatic variables. As a caveat, we note that this is not a formal attribution of the 2023 anomaly because no counterfactual exists for the year but rather an attribution of the change in BA in 2003–2019 with the additional influence of climate factors on BA in 2023 superimposed (this caveat also applies to the other focal regions). For Canada, the BA attribution targets cells in the 95th percentile of the BA fraction distribution.

#### Greece

Total climate forcing caused a change in the likelihood of high BA in Greece of 2.7 [–0.2 to 6.0] % in the period 2003–2019 in the cells with the greatest BA fraction (90th percentile), with 90 % confidence (Fig. 14). In this case we use the 90th percentile to represent high BA over the region for the month of August, over 2003–2019. This increase is likely a conservative figure given the additional warming since 2019, and estimating the additional burning that might have been experienced during the anomalous conditions of 2023, we find an additional change of 1.3 [–9.3–11] % (Fig. 10). Socioeconomic factors likely (79.9 %) caused a decrease in burning, though they could have caused an increase, affecting BA extent by –3.1 [–10.9 to 5.1] %.

Overall we estimate that BA in Greece in August 2023 was increased by 4 [–9.5 to 17.7] % due to total climate forcing in the 2003–2019 period combined with this year's anomaly in the climatic variables. In the case of Greece, uncertainties around the influence of total climate forcing and socioeconomic factors are greater because the smaller region size limits information available for model optimisation. For Greece, the BA attribution targets cells in the 90th percentile of the BA fraction distribution.

#### Western Amazonia

Over the period 2003–2019, total climate forcing was virtually certain to have caused an increase in burned areas like the one experienced in western Amazonia in September and October 2023 (> 99.9 % likelihood), with a likely range of increase in extent of 3.5 [1.4–9.9] % (Fig. 14). Here we assess the change in BA due to total climate forcing in the cells that burn most regularly (95th percentile) of our defined region of western Amazonia over September and October 2003–2019.
<figure><img src="image_17.png" /><figcaption>**Figure 13.** The high FWI in 2023: (a) 95th percentile FWI in June over Canada, (b) 90th percentile FWI in August over Greece, and (c) 95th percentile FWI in September–October over western Amazonia in the HadGEM3 ensemble of ALL (anthropogenic and natural forcing, orange) and NAT (natural-only forcing, blue) bias-adjusted simulations and ERA5 reanalysis (black line).</figcaption></figure>

Extending our analysis to the 2023 anomaly, we estimate that additional burning could have been up to 7.4 [0.8–36.2] % on top of the 2003–2019 levels. Despite finding little influence of humans specifically in 2023 compared to the previous 10 years in Fig. 10, since the early industrial, we show socioeconomic factors have had a large influence on the occurrence of extreme levels of burning. Events similar to 2023 were very likely exacerbated by socioeconomic conditions (92.0% likelihood) increasing BA by 2.9 [0.1–7.8] %.

Overall, we estimate that BA in western Amazonia in September–October 2023 was increased by 11.1 [2.2–49.7] % due to total climate forcing in the 2003–2019 period combined with this year’s anomaly in the climatic variables. For western Amazonia, the BA attribution targets cells in the 95th percentile of the BA fraction distribution.

### 4.2.3 Background changes in burned area due to total climate forcing, socioeconomic factors, and all forcing

#### Canada

As reported in Burton et al. (2024), we also show how background levels of fire extent, represented by the modelled median BA for months of interest, have changed overall in Canada due to total climate forcing (Fig. 15), socioeconomic forcing (Fig. S15), and all forcings (Fig. S16). Using AR6 regions that best match our focus areas, we show that BA has increased by 1.9 % [0.1, 3.6] in northwest North America (NWN) due to total climate forcing but reduced by −0.2 % [−1.7, 1.3] in northeast North America (NEN) (Fig. 15). In these regions, socioeconomic forcing has dampened the effects of climate change, by reducing BA by −9.5 % [−13.6, −6.3] in NWN and −8.5 % [−12.5, −5.7] in NEN (see Fig. S15). All forcings combined have led to an overall reduction in BA of −8.3 % [−12.5, −4.9] in NWN and −8.7 % [−12.8, −5.8] in NEN (Fig. S16).

#### Greece

Burton et al. (2024) find a larger increase in median BA for months of interest due to total climate forcing in the Mediterranean region (MED), with an increase of 14.5 % [11.5, 18.1] today compared to the counterfactual (Fig. 15). This is particularly the case for the highly burned areas, where the increase is larger compared to the lower end of the distribution. However, socioeconomic factors have largely offset this by reducing BA by −10.2 % [−13.6, −6.6] (see Fig. S15). All forcings combined have led to an overall regional increase in BA of 0.5 % [−3.5, 5.5] (see Fig. S16).

#### Western Amazonia

As per Burton et al. (2024), total climate forcing has increased median BA for months of interest by 11.5 % [5.4, 18.4] in northwest South America (NWS) today compared to the counterfactual (Fig. 15). Again, this increase is mostly impacting the BA at the higher end of the distribution. This is mostly offset by socioeconomic factors (−9.0 % [−18.9, 1.2]), although all forcings combined have still led to an overall increase in BA of 1.5 % [−6.9, 10.5] in the region (see Supplement).

## 5 Seasonal and multidecadal outlook

### 5.1 Methods

#### 5.1.1 Seasonal forecasts

Among the modes of variability in the climate system most relevant to wildfire activity globally is the El Niño–Southern Oscillation (ENSO) (Mariani et al., 2016; Fuller and Murphy, 2006; Cardil et al., 2023). Numerous studies have demonstrated that there is a predictable cascade of fire across tropical continents during ENSO events, highlighting staggered responses of wildfire to ENSO (Chen et al., 2017). The utility of using ENSO as a predictor of fire is highlighted by
<figure><img src="image_18.png" /></figure>

**Figure 14.** Estimated change in BA in selected months in the period 2003–2019 (e.g. 17 months of June for Canada) as modelled by ConFire when driven by the ISIMIP3a reanalysis versus counterfactual scenarios. The panels show the change in BA extent (%) due to (a, b, c) total climate forcing versus a scenario without climate forcing and (d, e, f) socioeconomic forcing versus a scenario without socioeconomic forcing. Results are shown for (a, d) Canada, (b, e) Greece, and (c, f) western Amazonia. Many points are plotted because each point represents a posterior estimate of change in BA for a month, and there are 1000 iterations of ConFire to explore the effect of uncertainty in input parameters and structural relationships between BA and input variables. Impact values are the central (median) and 5th–95th percentile range of the estimated values of change in BA (%). The likelihoods shown are the probability of the change being significant, measured as the percentage of the posterior estimates being above/below zero when the median is above/below zero.

its application in Indonesia, where severe fires during the 2015 El Niño led to hazardous haze in Singapore and diplomatic tensions in the region, prompting better regional co-operation and enforcement of anti-burning laws (Field et al., 2016; Forsyth, 2014; Carmenta et al., 2021). Consequently, Indonesia now implements preemptive bans on agricultural burning based solely on ENSO predictions, a measure that proved successful in 2023 when no significant fire anomalies were recorded despite a strong positive ENSO event (Lin et al., 2020; Sloan et al., 2022).

Another phenomenon demonstrably linked to global fire activity is the Indian Ocean Dipole (IOD), which occurs in the Indian Ocean. There is ongoing debate regarding the direct influence of the IOD on Australian fires, for example as the signal is often modulated by changes in land management practices (Harris and Lucas, 2019). Other atmospheric modes of variability in the Southern and Northern Hemisphere and in the Arctic regions can also have influence on interannual BA patterns, and Fig. S1 in the Supplement shows the climate modes with strongest influence on regional BA globally.

Outputs available from the Copernicus Climate Change Service (C3S) multi-model seasonal prediction system are

used to evaluate large-scale climate modes with the most proven links to variation in fire activity: ENSO and IOD (Hersbach et al., 2023). As not all regions display similar seasonal direct correlations between fire activity and ENSO, we also use seasonal outlooks of the FWI from one of the models, ECMWF-SEAS5, to identify probabilities for the establishment of anomalous landscape flammability in the next season. This is done using a 51-member forecast ensemble and a 24-year model climatological distribution (derived from a 25-member ensemble re-forecast) covering the period 1993–2016. The probability of exceedance is determined based on the proportion of forecast members meeting an distributional threshold at any given geographical point. We consider the 75th percentile threshold indicative of moderate anomalous conditions and the 95th percentile indicative of extreme anomalous conditions.

### 5.1.2 Decadal projections of burned area

In order to project future changes in BA, we utilised the same modelling approach detailed in Sect. 4.1.3, “Peaks in burned area during 2023–2024”, following a similar protocol to UNEP (2022a). We drive the ConFire model with
<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /><figcaption>**Figure 15.** Change in median BA due to total climate forcing from FireMIP. Present-day BA (2003–2019) for factual (historical forcing, orange) and counterfactual (detrended climate, blue), for AR6 regions. Panels show (a) northwest North America (NWN), (b) northeast North America (NEN), (c) Mediterranean (MED), and (d) northwest South America (NWS). Probability is shown on a log scale.</figcaption></figure>

ISIMIP3a and bias-corrected JULES-ES data. For predictive mode, we used bias-corrected global climate model (GCM) outputs from ISIMIP3b. While ISIMIP3a provides reanalysis datasets to drive models for impact assessments, ISIMIP3b provides driving data from five bias-corrected GCMs, including historical data up to 2014 and future scenarios from 2015–2100 under Shared Socioeconomic Pathway (SSP) scenarios SSP126, SSP370, and SSP585. Each SSP represents future socio-economic pathways and includes greenhouse gas (GHG) emissions to drive the GCMs. The five GCMS used are GFDL-ESM4 (Held et al., 2019), IPSL-CM6A-LR (Boucher et al., 2020), MPI-ESM1-2-HR (Mauritsen et al., 2019), MRI-ESM2-0 (Yukimoto et al., 2019), and UKESM1-0-LL (Good et al., 2019; Sellar et al., 2019). As part of ISIMIP3b, each GCM is bias-corrected as described in Lange (2019).

At present, future projections for land use and population density forcing were not available for ISIMIP3b, so we only considered the influence of climate and vegetation fuel loads (related to land cover) on fire and not changes in ignitions or

land use. We used JULES-ES land cover outputs as per the previous section but this time with JULES driven by each of the five different bias-corrected GCMs and for the three different SSP scenarios instead of historical reanalysis. The land cover output was then bias-corrected (using the same mapping procedure as Sect. “Peaks in burned area during 2023–2024”, based on biases between JULES-ES driven by reanalysis and VCF observations) to maintain consistency with the GCM bias correction procedures. We apply an additional bias correction to preserve the trend in vegetation cover from the historical period and to smooth the transition between the historical and future periods (see Supplement Sect. 1.1.1 for details). The results in Sect. 5.2.2 are for the months June–August for Canada, July–September for Greece, and August–October for western Amazonia, corresponding to those regions’ fire seasons today.

Our approach provides a probability distribution of future BA representing the uncertainty range from cross-model (GCM) spread in the response of climate and vegetation to emissions for each scenario and year in the period 2010–
2100. The years 2010–2014 were consistently adopted from the historical experiment.

For the western Amazonia focal event, we additionally tested a 1-in-100-year event under 2010–2020 climate, defined as the BA at the 99th percentile ConFire distribution. We also use the 1-in-100 definition at a grid cell level to determine spatial variations in the change in extreme fire for each region. We then calculated decadal average likelihoods of the regions’ event in each decade up to 2100. Return times are 1 over the likelihood. The change in likelihood of an event occurring on a given return time was calculated relative to the 2010–2020 baseline period.

For the Canada focal event, we also calculated the integrated probability of an event with similar magnitude to 2023 within the expected lifespan of a Canadian citizen. According to UN population statistics, the average life expectancy of a Canadian citizen born today is 83 years (United Nations Population Division, 2022). In order to cover the 7-year period after 2100, we extrapolated the annual trend in probabilities. The integrated probability is calculated as 1 minus the product of the annual probability of not seeing a fire event like 2023, for each year between 2023 and 2106.

## 5.2 Results

### 5.2.1 Seasonal outlook for 2024

The 2023–2024 El Niño event emerged as the fourth most powerful on record, causing widespread droughts, floods, and other anomalous conditions worldwide. Officially declared by the World Meteorological Organization (WMO) on 4 July 2023, its meteorological impacts unfolded between November 2023 and April 2024 (Joshi, 2023). Climate scientists have found that the 2023–2024 El Niño event, superimposed on climate change signal, has elevated global temperatures beyond the records set during the 2016 El Niño event. Global mean surface temperatures in 2023 were 1.31 °C above pre-industrial levels of 1850–1900 (Forster et al., 2024).

As of mid-June 2024, El Niño conditions have transitioned toward neutral conditions, which are forecasted to persist during the boreal summer. The Indian Ocean Dipole (IOD) index is currently positive, and forecasts indicate that it will remain in a positive state for the next season. Connections are established between a positive IOD phase and fire risk in Indonesia and parts of Australia, though outcomes generally depend on interactions with the ENSO phase (Pan et al., 2018; Ren et al., 2024; Abram et al., 2021). Similarly, the positive phase of the IOD is linked with heightened fire risk in South America, particularly in the Amazon basin, where it can interact with other climate teleconnections to exacerbate droughts (Cardil et al., 2023; Dong et al., 2021).

Figure 16 shows the predicted probabilities of the monthly average FWI exceeding moderate (75th percentile) or high (95th percentile) thresholds of the monthly climatology.

Most areas in southeast Asia and South America were predicted to experience a decrease in the likelihood of anomalous conditions over May, June, and July 2024. Parts of Canada are predicted to reach moderate anomalous conditions once again in early summer, and this combined with overwintering fires could promote a second consecutive high fire season, as has already been reported in the media (BBC News, 2024, Austen, New York Times, 2024). Predictions also suggest that the moderate FWI threshold will be exceeded in southeast, central, and western Brazil, with the high threshold exceeded in southern and western parts. In parts of Africa, moderate FWI anomalies may be experienced throughout June–August.

### 5.2.2 Future changes in likelihood of extreme fire events

#### Canada

The probability of Canada experiencing BA extent similar to June 2023 (specifically, for cells with BA fraction in the 95th percentile in that month) is estimated to be 0.15% in any given year under the climate conditions of 2010–2020, according to estimates made using reanalysis data (Table 6; Fig. 17). Bias correction did not fully resolve all discrepancies between the GCMs and reanalysis data, and the GCMs gave a range of likelihoods spanning 0.02% to 0.71% for any given year under the climate conditions of 2010–2020. We describe future changes as significant if the range across GCM projections for a future period does not overlap with the range given by the GCMs for 2010–2020.

By the 2040s, the likelihood of an event like 2023 increases significantly to 0.42%–2.2% across scenarios, which is 2–6 times as likely as in the 2010s (Table 6; Fig. 17). While the likelihood of an event like 2023 occurring in the 2040s is slightly higher in SSP585 (0.6%–2.2%) than other scenarios (0.41%–1.6% for SSP126 and 0.5%–1.7% for SSP370), differences between future scenarios are not significant at the mid-century point.

The SSP126 scenario diverges significantly from SSP370 and SSP585 after 2070. Under SSP126, the likelihood of an event like 2023 stabilises at 0.3%–0.8% in the 2070s and remains largely unchanged until the 2090s (Fig. 17). In contrast, the likelihood of an event like 2023 continues to rise to 2.1%–3.7% in the 2090s under SSP585 (Table 6, Fig. 17). Under SSP126, the probability of at least one event like 2023 recurring in any year (of any decade) between 2024 and 2100 is estimated to be 18%–73%, compared with 59%–87% under SSP585. Hence, the probability of an event like 2023 recurring by the 2090s is estimated to be 2 times greater in SSP585 than in SSP126.

Someone born in Canada in the current decade, with a life expectancy of 83 years (United Nations Population Division, 2022), has a 65%–90% probability of seeing a similar event in their lifetimes under SSP585, compared with only an 12% likelihood of someone who reached 83 years
<figure><img src="image_23.png" /><figcaption>**Figure 16.** Probability for the monthly average FWI exceeding the (a–c) 95th percentile threshold (anomalous conditions) and (d–f) 75th percentile threshold (extremely anomalous conditions) of the monthly climatological distributions. The probability is calculated using the 51-ensemble-member realisation from ECMWF’s long-range forecasting system, ECMWF-SEAS5 FWI, and comparing it with the 1991–2016 climatology (Copernicus Emergency Management Service, 2019).</figcaption></figure>

old in the 2010s. Someone born in Canada today would also have a 42%–80% probability of seeing an event of similar magnitude twice under SSP585. Under SSP370, a citizen has a 48%–84% probability of seeing a similar event once and 29%–76% probability of seeing a similar event twice. This reduces to 19%–76% for one occurrence and 4%–58% for two occurrences under SSP126.

These differences outlined above highlight the divergence in future likelihood of a 2023-like event in Canada between high-mitigation (SSP126) and no-mitigation (SSP585) scenarios. The divergence of likelihoods between the two scenarios is associated with increases in both fuel load and fuel dryness (Fig. 17).

Future changes in probability of an event like 2030 under the SSP370 lie closer to those of SSP585 than SSP126 (Table 6; Fig. 17). For example, the probability of an event like 2023 occurring at least once by the 2090s is estimated to be between 48% and 84% under SSP370, only slightly below the range projected under SSP585. This highlights that high-mitigation, low emissions pathways are required to limit future increases in the potential for high-impact events in Canada this century.

Figure 18 shows the spatial variability of changes in summer BA and 1-in-100-year BA events in Canada. While some areas see increases in BA and fire extremes in all scenarios, the greatest rates of change are projected in southern Alberta and Saskatchewan and under SSP585. These patterns emerge as early as 2030 (Fig. S17). Meanwhile, the Yukon and Northwest Territories are projected to see increased BA from 2040 in all scenarios, with 1-in-100-year BA events becoming around twice as likely in parts of these provinces (Fig. S18). By the end of the century, a larger increase in BA is seen under SSP585 and SSP370 than in SSP126, with 1-in-100-year BA events becoming up to 5 times more likely

in parts of Yukon and Northwest Territories. Uniquely under SSP585, factor 2 increases in BA and 1-in-100-year BA events extend into British Columbia.

## Greece

The probability of Greece experiencing BA extent similar to August 2023 (specifically, for cells with BA fraction in the 90th percentile in that month) is estimated to be 1.3% in any given year under the climate conditions of 2010–2020, according to estimates made using reanalysis data (Table 6; Fig. 17). Bias correction did not fully resolve all discrepancies between the GCMs and reanalysis data, and the GCMs gave a range of likelihoods spanning 0.7%–1.8% for any given year under the climate conditions of 2010–2020.

In SSP126, no significant increase in likelihood of an event like 2023 is projected for any decade through 2100 (i.e. beyond the range of 0.7%–1.8% for the 2010s). This is despite the likelihood of a 2023-like event in some decades being as high as 2.3 times more likely than in the 2010s under SSP126 (Fig. 17). The lack of significance in these changes may in part reflect our strict definition of significance (i.e. no overlap with the range of the 2010s). When likelihoods vary considerably across models due to the incomplete resolution of biases, the thresholds for significance are high. The small number of observations available for model training in Greece is due to its small domain size, which likely contributes to wider uncertainty bounds and higher biases than in the other, larger focal regions. Overall, Greece could see large increases in the occurrence of extreme BAs even under strong mitigation (SSP126), but uncertainties are large.

SSP350 and SSP585 show significant increases in the likelihood of an event like 2023 by the 2070s (relative to the 2010s) and also diverge significantly beyond SSP126 in the
<table><caption>**Table 6.** Summary of the likelihood of extreme events today using reanalysis “factual” and today and into the future using bias-corrected GCMs for our three focal regions. “2023” events focuses on the BA extreme identified in Sect. 3.4.3. 1-in-100 for western Amazonia additionally looks at the likelihood of a 1-in-100 event under present-day climate conditions, following the definition of extreme in UNEP (2022a). We also determine how much more frequent the events will be at two different time horizons based on each models likelihood in the future projections over likelihood during 2010–2020. Asterisks (*) indicate non-significant changes from 2010–2020 values. Colours show linear increase in likelihood (red) and frequency (orange), where darker shade indicates higher values.</caption><thead><tr><th rowspan="3">Region</th><th rowspan="3">Event</th><th rowspan="3">SSP</th><th rowspan="3">Represents</th><th colspan="8">Likelihood (%/year)</th><th colspan="4">How much more frequent (multiplier)</th></tr><tr><th colspan="2">2010-2020</th><th colspan="2">2040-2050</th><th colspan="2">2090-2100</th><th colspan="2">2040-2050</th><th colspan="2">2090-2100</th></tr><tr><th>min</th><th>max</th><th>min</th><th>max</th><th>min</th><th>max</th><th>min</th><th>max</th><th>min</th><th>max</th></tr></thead><tbody><tr><td rowspan="4">Canada</td><td rowspan="4">2023<br>(~1<br>in-700)</td><td>Factual</td><td>observed</td><td>0.15</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSP126</td><td>strong mitigation</td><td>0.1</td><td>0.61</td><td>0.42*</td><td>1.6*</td><td>0.22*</td><td>2*</td><td>2.6*</td><td>4.2*</td><td>2.2*</td><td>3.3*</td></tr><tr><td>SSP370</td><td>middle of the road</td><td>0.12</td><td>0.54</td><td>0.5*</td><td>1.7*</td><td>1.3</td><td>3.4</td><td>3.1*</td><td>4.2*</td><td>6.3</td><td>10.8</td></tr><tr><td>SSP585</td><td>no mitigation</td><td>0.1</td><td>0.71</td><td>0.6*</td><td>2.2*</td><td>2.1</td><td>3.7</td><td>3.1*</td><td>6*</td><td>5.2</td><td>21.1</td></tr><tr><td rowspan="4">Greece</td><td rowspan="4">2023<br>(~1<br>in-80)</td><td>Factual</td><td>observed</td><td>1.3</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSP126</td><td>strong mitigation</td><td>0.91</td><td>1.7</td><td>1.4*</td><td>1.8*</td><td>1.4*</td><td>1.9*</td><td>1.1*</td><td>1.5*</td><td>1.2*</td><td>1.6*</td></tr><tr><td>SSP370</td><td>middle of the road</td><td>0.99</td><td>1.5</td><td>0.87*</td><td>2.2*</td><td>2.5</td><td>3.1</td><td>0.9*</td><td>1.5*</td><td>2.1</td><td>2.6</td></tr><tr><td>SSP585</td><td>no mitigation</td><td>0.67</td><td>1.8</td><td>1.2*</td><td>2.4*</td><td>2.9</td><td>3.3</td><td>1.3*</td><td>1.7*</td><td>1.8</td><td>4.3</td></tr><tr><td rowspan="4">Western<br>Amazonia</td><td rowspan="4">2023<br>(~1<br>in-6)</td><td>Factual</td><td>observed</td><td>16.6</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSP126</td><td>strong mitigation</td><td>15.1</td><td>16.6</td><td>15.8*</td><td>17.9*</td><td>15.9*</td><td>17.6*</td><td>1.1*</td><td>1.1*</td><td>1.1*</td><td>1.1*</td></tr><tr><td>SSP370</td><td>middle of the road</td><td>15.2</td><td>16.3</td><td>16.2*</td><td>18.1*</td><td>17.7</td><td>20.4</td><td>1.1*</td><td>1.1*</td><td>1.2</td><td>1.3</td></tr><tr><td>SSP585</td><td>no mitigation</td><td>15.1</td><td>16.5</td><td>16.4*</td><td>18.3*</td><td>18.2</td><td>21</td><td>1.1*</td><td>1.1*</td><td>1.2</td><td>1.3</td></tr><tr><td rowspan="4">1-in-<br>100</td><td rowspan="4"></td><td>Factual</td><td>observed</td><td>1.5</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SSP126</td><td>strong mitigation</td><td>0.82</td><td>1.5</td><td>1.2*</td><td>2.2*</td><td>1.2*</td><td>2*</td><td>1.4*</td><td>1.5*</td><td>1.3*</td><td>1.5*</td></tr><tr><td>SSP370</td><td>middle of the road</td><td>0.81</td><td>1.5</td><td>1.3*</td><td>2.2*</td><td>2</td><td>3.1</td><td>1.5*</td><td>1.6*</td><td>2.2</td><td>2.4</td></tr><tr><td>SSP585</td><td>no mitigation</td><td>0.8</td><td>1.5</td><td>1.4*</td><td>2.4*</td><td>2.3</td><td>3.3</td><td>1.6*</td><td>1.8*</td><td>2.2</td><td>2.9</td></tr></tbody></table>

2080s. SSP585 and SSP370 do not diverge from one another throughout this century, and, in 2100, both scenarios give a likelihood of an event like 2023 of 2.5%–3.3%. This range is equivalent to a 1.8-to-4.3-fold increase of the values of the 2010s and an average return time of 31–39 years. The divergence of likelihoods between SSP126 and the two other scenarios (SSP350 and SSP585) is associated with increases in both fuel load and fuel dryness, with particularly striking differences in the latter across the scenarios (Fig. 17).

There are some spatial patterns in the future trajectory of summer BA and the likelihood of future 1-in-100-year BA events in Greece by the 2090s (Fig. 19). Interior parts of Greece tend to see a decline in BA, while coastal parts see an increase in BA, and this pattern broadly holds for 1-in-100-year events. Across scenarios with increasingly low levels of climate change mitigation, there is an expansion to the portion of Greece’s total area that experiences increased summer BA and increased frequency of 1-in-100-year events.

## Western Amazonia

The probability of western Amazonia experiencing BA extent similar to September–October 2023 (specifically, for cells with BA fraction in the 95th percentile in that month) is estimated to be 16.58% in any given year under the climate conditions of 2010–2020, according to estimates made using reanalysis data (Table 6; Fig. 17). Bias correction did not fully resolve all discrepancies between the GCMs and reanalysis data, and the GCMs gave a range of likelihoods

spanning 15.1%–16.6% for any given year under the climate conditions of 2010–2020. As expected, these results suggest that the events observed in Amazonia in 2023 were not as extreme as those in Canada and Greece, with returns times of around 6–7 years.

We note that spatial patterns of fire are highly dependent on patterns of land use and human ignition sources in Amazonia (e.g. Fig. S14), and an important caveat is that the BA responses to future climate and land cover change, presented below, are likely to be highly modulated by socioeconomic factors (Lapola et al., 2023; Kelley et al., 2021; Silveira et al., 2020).

Under the SSP585 scenario, the likelihood of an event like 2023 increases significantly in the 2090s to 18.2%–21.0%, representing a factor 1.2–1.3 increase versus the 2010s. This increase is primarily attributed to lower fuel moisture, in line with declines in fire weather in this region (Sect. 4.2.1). On the other hand, in the SSP126 scenario representing strong climate change mitigation, the likelihood of an event like 2023 does not change significantly at any point this century (Table 6).

The likelihood of an event like in 2023 increases substantially in the centre of the region by the 2030s under SSP370 and SSP585 (Fig. S21), and the affected area expands through 2100 (Fig. S22). Southern regions with greater population and infrastructure densities see lesser increases in likelihood. Perhaps more important for the region’s fire-sensitive forests is the projected increase in BA in SSP370 and SSP585 across forests in the north of the region, which
<figure><img src="image_24.png" /><figcaption>**Figure 17.** Future projections from ConFire of the change in likelihood of BA extent of the magnitude seen in 2023–2024 and the contribution of fuel and moisture controls towards those changes. Each set of bars shows changes for each decade relative to the 2010–2020, with each bar representing a different SSP scenario and the spread of bars indicating the variation across GCMs, with individual bars representing different GCMs.</figcaption></figure>

would be expected to impact some of Earth’s most remote and pristine forests by the end of this century (Fig. S23).

The 2023 event in Amazonia had a relatively high return interval compared with other focal regions. Hence we tested for change in likelihood of a rarer, 1-in-100-year event in western Amazonia. Under SSP585, we found that the likelihood of a 1-in-100-year event increased significantly by the end of the century to 2.3%–3.3% in the 2090s (representing a factor 2.2–2.9 increase in likelihood). The likelihood of a 1-in-100-year event also increased significantly under SSP370 towards the end of this century, but under SSP126 no significant change occurred this century.

Our results show that SSPs associated with lesser climate change mitigation (SSP585 and SSP370) promote more frequent extreme BA in western Amazonia, irrespective of change in land use and human ignition factors, with potential to strongly modulate the BA response to climate. There is greater potential for compounding effects of human factors and climate-driven increases in extreme BA likelihood under scenarios with no or low climate change mitigation than in the case of scenarios with high climate mitigation (such as SSP126).

## 6 Data availability

BA data from NASA's MODIS BA product (MCD64A1) are extended from Giglio et al. (2018) and are available at Giglio et al. (2021, https://lpdaac.usgs.gov/products/mcd64a1v061/, last access: 9 July 2024). GFED4.1s fire C emissions data are extended from van der Werf and are available at https://globalfiredata.org/ (GFED, 2024). GFAS fire C emissions data are extended from Kaiser et al. (2012) and are available at https://confluence.ecmwf.int/display/CKB/CAMS+global+biomass+burning+emissions+based+on+fire+radiative+power+%28GFAS%29%3A+data+documentation (ECMWF, 2024). The Global Fire Atlas data are extended from Andela et al. (2019) and are available from Andela and Jones (2024, https://doi.org/10.5281/zenodo.11400062). Regional summaries of the MODIS BA, GFED4.1s, GFAS, and the Global Fire Atlas presented here are available from Jones et al. (2024, https://doi.org/10.5281/zenodo.11400539). Studies utilising our regional summaries should cite both the current article and the primary reference for the variable(s) of interest: Giglio et al. (2018) for BA; van der Werf et al. (2017) for GFED4.1s fire C emissions; Kaiser et al. (2012) for GFAS fire C emissions; and Andela et al. (2021) for the 2023 event in Amazonia.
<figure><img src="image_25.png" /><figcaption>**Figure 18.** Projected changes in June–August BA over Canada by 2090–2100 under three SSP scenarios, with BA simulated by ConFire. (a, **d**, **g**) Average June–August BA fraction (%) for 2010–2020. (b, **e**, **h**) Relative change in June–August BA extent projected for 2090–2100 period, expressed as a multiplier of 2010–2020 values. (c, **f**, **i**) Increased (or decreased) frequency in the 2090–2100 period of a 1-in-100-year event defined for the period 2010–2020, expressed as a multiplier of 2010–2020 values. In the left column, the size of the dot in each grid cell indicates the likelihood (larger means a higher likelihood) of a BA fraction (or being greater than the threshold indicated by the coloured dot; see legend at the base). Likewise, the size of the dot varies with likelihood that the BA fraction exceeds the threshold indicated by the coloured dot (see legend at the base). For example, a large, pale-orange dot in the left column indicates a high likelihood of the BA fraction exceeding 0.05 %, whereas a small, dark-red dot indicates a small (but non-zero) likelihood of the BA fraction exceeding 0.5 %.</figcaption></figure>

al. (2012) for GFAS fire C emissions; Andela et al. (2019) for the Global Fire Atlas.

Driving data and re-gridded BA target data for ConFire and ConFire outputs are available from Kelley et al. (2024a, https://doi.org/10.5281/zenodo.11420742). Historical (1960–2013) HadGEM3-A data are available through the Centre for Environmental Data Analysis (CEDA) archive of the NERC’s Environmental Data Service (EDS) at http://catalogue.ceda.ac.uk/uuid/99b29b4bfeae470599fb96243e90cde3 (Met Office, 2016). FireMIP and ISIMIP driving and output data are available from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) repository at https://data.ISIMIP.org/ (ISIMIP, 2024).

## 7 Code availability

ConFire attribution framework code (Kelley et al., 2021; Barbosa, 2024) was incorporated into the FLAME repository (https://github.com/douglassk3/Bayesian_fire_models/tree/

ConFire, last access: 9 July 2024) and is archived at Zenodo (https://doi.org/10.5281/zenodo.11460232, Barbosa et al., 2024). Configuration settings for Sect. 3 are in namelists/nrt.ini, while settings for Sects. 4 and 5 are in namelists/ISIMIP.ini. Scripts for reproducing plots can be found in the State of Wildfires GitHub repository (https://github.com/douglassk3/State_of_Wildfires_report (last access: 9 July 2024) and https://doi.org/10.5281/zenodo.11460379, Kelley et al., 2024b).

The code used to produce the FWI attribution results is available in the State of Wildfires GitHub repository (https://github.com/douglassk3/State_of_Wildfires_report (last access: 9 July 2024) and https://doi.org/10.5281/zenodo.11460379, Kelley et al., 2024b). FWI code can be accessed via the ECMWF GitHub (https://github.com/ecmwf-projects/geff, Di Giuseppe and Maciel, 2022). All details of the data and code used for the FireMIP attribution results are documented in Burton et al. (2024).
<figure><img src="image_26.png" /><figcaption>Figure 19. Same as Fig. 18 but for Greece and the months July–September.</figcaption></figure>

The current version of ibicus, used for JULES-ES bias correction, is available from PyPI (https://pypi.org/project/ibicus/, Spuler and Wessel, 2024a) under the Apache License version 2.0 and is described in detail at https://ibicus.readthedocs.io/en/latest/ (Spuler et al., 2024b). Ibicus is also archived on Zenodo by Spuler and Wessel (2023; https://doi.org/10.5281/zenodo.8101898). Model code and evaluation for bias correction of JULES-ES model output can be found on the State of Wildfires GitHub repository (https://github.com/jakobwes/State-of-Wildfires---Bias-Adjustment (last access: 9 July 2024), with an archived DOI available at https://doi.org/10.5281/zenodo.13255783, Spuler and Wessel, 2024b).

# 8 Conclusions

## 8.1 Summary of the state of wildfires in 2023–2024

### 8.1.1 Extreme wildfire events of 2023–2024

- *Global.* A total of $3.9 \times 10^6$ km$^2$ burned globally during the 2023–2024 fire season, slightly below the average of previous seasons ($4.0 \times 10^6$ km$^2$) and ranking 13th since 2002. Despite the lower BA, fire C emissions were 16% above average, totalling 2.4 Pg C, ranking seventh highest since 2003. Global C emissions were pushed up by record emissions in Canadian boreal forests and pulled down by below-average fire activity in the African savannas (significant because fires in the African savannas make, on average, the largest contribution of any continental biome to global mean annual BA and emis-
sions). If global savannah fire emissions had been in line with their average in 2023–2024, global fire C emissions would have been the highest on record.

- *North America.* There was record fire activity in Canada's boreal forests, with BA reaching 6 times the average and fire C emissions over 9 times the average and contributing significantly towards global C emissions (24%, up from a mean value of 3% from prior fire seasons). Canada experienced extreme and widespread fires, with over 150 000 km² burned, prompting evacuations of 232 000 people. Eight firefighters lost their lives. Fires in Canada led to a significant degradation of air quality, with populations living in the United States and Canada exposed to atmospheric concentrations of PM₂.₅ over daily standards for periods of 2 weeks to multiple months. The United States saw generally below-average fire activity, but the Lahaina wildfire in Maui, Hawaii, resulted in 100 civilian deaths, destroyed 2000 homes, and displaced 10 000 people. Texas recorded its largest ever single fire, which destroyed 130 homes and killed two civilians.

- *South America.* South America experienced somewhat below-average fire extent overall, but notable exceptions included significant anomalies in the northwest of the continent. In Brazil's State of Amazonas, fire counts reached record highs amidst drought conditions, severely impacting air quality in Manaus. In neighbouring parts of Bolivia, Peru, Venezuela, and Guyana, also affected by regional drought, record or high-ranking levels of fire counts, extent, and carbon emissions were observed. In Chile, the Valparaíso wildfire in February 2024 killed 131 people and caused widespread property destruction.

- *Europe.* Europe experienced low fire extent in 2023–2024; however the Evros fire in Greece set a new EU record for individual fire size (around 900 km²) and killed 19 people. Individual fires in Greece, Spain, Italy, Portugal, France, and Scotland led to a range of impacts including large-scale evacuations, significant suppression costs, disruption of water supplies, damage to infrastructure or agricultural lands, impacts on tourism and local economies, and destruction of properties (see Appendix A).

- *Oceania.* Above-average fire activity and extent were observed in the savannahs, grasslands, and shrublands of Western Australia and the Northern Territory of Australia. Although less impactful than the 2019–2020 Black Summer fires due to their remoteness, wildfires near Perth resulted in property destruction. The Tara and Mount Isa fires in Queensland destroyed 65 homes and claimed two lives. In Victoria, fires destroyed over 40 homes and injured five firefighters. In New South Wales, forest fires caused widespread smoke-related damages.

- *Asia.* Most parts of Asia saw low fire activity. Lao PDR, Thailand, and Vietnam experienced high fire counts amidst reported heatwave conditions and a possible uptick in agricultural fire use, leading to regional haze and air quality issues. In Mongolia's Dornod Province, wildfires burned large parts of the Daurian steppe, and firefighting was required to stop fires crossing the border into Russia.

- *Africa.* Africa experienced low fire extent in general during 2023–2024, with BA 13% below average in the African grassland, savannah, and shrubland biome. However, extreme fires in northern Africa, particularly Algeria and Tunisia, prompted significant emergency responses including assistance from the EU. In Algeria, wildfires occurring in temperatures around 50 °C resulted in 34 deaths and over 1500 evacuations, with over 8000 personnel deployed to combat the fires. Tunisia faced similar challenges with strong winds exacerbating wildfires, leading to evacuations in the northwestern region. In coastal South Africa, fires in the Western Cape caused structure damage and evacuations.

### 8.1.2 Diagnosing drivers and assessing predictability

- *In Canada, fire weather conditions were the primary drivers of the record-breaking fire activity and extent during 2023.* However, there were notable contributions from other drivers, such as upticks in ignitions or human factors that were not explicitly represented in our analyses. This highlights potential inadequacies in predicting some ignition sources or accurately representing fire propagation in current forecasting systems.

- *The exceptional nature of the Evros fire in Alexandria, Greece, could not have been predicted using fire weather forecasts.* While there were discernible indications in the Fire Weather Index (FWI) records that the days around the event were more extreme than most days in the fire season, similar conditions were also observed in other periods without resulting in the same catastrophic impacts. This highlights the intrinsic difficulties in forecasting isolated extreme fire events and underscores the need to advance early warning systems beyond fire weather to consider fuel availability and ignition variability.

- *The extreme fire season in western Amazonia was driven by prolonged drought conditions linked to the strong El Niño.* Many fires developed, triggered by lightning ignitions early in the season amidst high fire weather anomalies. Other than weather conditions that acted as persistent controls for fire activity, several periods with high active fire counts were not predicted in late August and throughout September, likely due to the result of unrepresented ignition sources.
- In all focal events, extreme burned areas were driven by anomalies in multiple controls on fire. Weather, fuel moisture, and fuel abundance were all critical factors. The synchronous occurrence of anomalies in fire weather, high fuel load, and low fuel moisture created the conditions leading to the anomalous burned areas recorded in all three events. This underscores the fact that no single bioclimatic factor can explain the most severe fires. Instead, multiple contributing controls must coincide for the most extreme events to arise. Factors such as ignitions, suppression, and landscape fragmentation, related to human activities, likely played important roles in modulating the western Amazonian and Greece events.

- Fuel load is an important modulator of the relationship between fire extent and fire weather. Higher and/or drier fuel loads combined with high fire weather conditions caused the unprecedented extent of burning in Canada and western Amazonia. The boundaries (extinction points) of extreme fires in Canada and Greece often corresponded to areas with lower fuel loads, demonstrating that discontinuity in fuel availability constrained fire spread.

### 8.1.3 Attribution to global change

- In Canada, anthropogenic forcing increased the chance of high fire weather in 2023. Total climate forcing led to higher BA, whereas socio-economic factors may have decreased burning. Anthropogenic forcing (resulting from greenhouse gas emissions and land-use change) increased the probability of experiencing high fire weather in June 2023 3-fold. It is virtually certain that total climate forcing (resulting from climate change since the pre-industrial period) increased the BA in Canada by up to 40.1%. Socioeconomic factors related to land use, ignitions and suppression may have reduced burning, though with low confidence.

- In Greece, anthropogenic forcing increased the chance of high fire weather in 2023. Total climate forcing led to higher BA, and socio-economic factors may have increased or decreased BA. Anthropogenic forcing increased the probability of experiencing high fire weather in August 2023 by 1.9–4.1 times. It is likely that total climate forcing increased the BA in Greece by up to 17.7%, whereas socio-economic factors could have led to an increase or decrease. Climate change has increased average BA in the wider Mediterranean region, but this has been mainly offset by socio-economic factors.

- In western Amazonia, anthropogenic forcing has greatly increased the chance of high fire weather in 2023. It is virtually certain that total climate forcing led to higher BA, and very likely that socio-economic factors also contributed. Anthropogenic forcing increased the probability of experiencing high fire weather by more than a factor of 20. It is virtually certain that total climate forcing increased BA in western Amazonia by up to 49.7%, and very likely that socio-economic factors exacerbated the increase. Climate change has increased today's average BA in the region, and all forcings have led to an overall increase in burning.

### 8.1.4 Seasonal and multidecadal outlook

- While the positive El Niño phase of ENSO is subsidishing towards a neutral phase in 2024, the Indian Ocean Dipole is persisting in its positive phase, with potential to influence global fire patterns. The 2023–2024 El Niño event emerged as the fourth-largest positive anomaly on record; however most simulations forecast a transition to ENSO-neutral conditions in 2024. Positive IOD phases are associated with elevated BA Amazonia, Indonesia, and parts of Australia, though these tend to depend on interactions with ENSO.

- Seasonal predictions of fire weather through August 2024 highlight moderate positive anomalies (75th percentile) in parts of Canada and much of South America including Amazonia, as well as in central Africa, southern Africa, southeast Europe, western Australia, southeast Asia, and northeast Asia. Extreme (95th percentile) anomalies are rare in the forecast through August 2024.

- In Canada, the future likelihood of events like in 2023 is increased by rising fuel load and dryness, with high mitigation pathways significantly reducing these risks. The likelihood of extreme fire events similar to those in June 2023 is currently low (0.15% for any given year in the 2010s or 1 in 700 years). The likelihood is expected to increase to 0.42%–2.2% by the 2040s and thereafter continue to increase under high emissions scenarios (SSP585) reaching 2.1%–3.7% by the 2090s. The probability of experiencing such events at least once by the 2090s is estimated to be 65%–90% under SSP585, compared to 19%–76% under SSP126. Individuals born in Canada during the current decade have a 48%–84% likelihood of witnessing an event on the scale of 2023 again in their lifetime under SSP370 (a scenario close to present-day trajectories without additional mitigation efforts).

- In Greece, high mitigation scenarios result in no significant change in the likelihood of an event like 2023 through 2100, whereas scenarios without mitigation lead to significant increases. The current likelihood of extreme fire events like those in August 2023 is around 1.3% annually for any given year in the 2010s (or
roughly 1 in 100 years). Under SSP126, there is no sig-
nificant increase projected through 2100, whereas sig-
nificant increases in likelihood (to up to 3.3 %) are pro-
jected under SSP585 and SSP370 by the 2090s, im-
plying more frequent extreme fire events. Coastal ar-
eas of Greece are expected to experience the greatest
increases in risk, whereas interior regions may experi-
ence declines.

- *In western Amazonia, the effects of climate change on extreme fire likelihood in future scenarios are chiefly governed by fuel moisture effects, which are likely to be strongly modulated by local changes in land use and human ignition sources. The potential for compounding effects between fuel dryness and local stress factors is minimised in high-mitigation scenarios.* The current likelihood of extreme fire events like those in September–October 2023 is 16.6% (or roughly 1 in 6 years). Under SSP585, the likelihood of such events increases significantly to 18.2%–21.0% by the 2090s. In addition, there is a significant rise in the probability of 1-in-100-year events by the end of the century under SSP585 and SSP370. No significant rise in probability of 1-in-6 or 1-in-100 events is seen under SSP126. Hence, while increased fire risks related to climate change in the Amazon can be compounded by human activities, this is least likely under SSP126. The impact of extreme fire events is expected to be severe in pristine northern forests, emphasising the need for strong climate change mitigation.

- Overall, high-emissions scenarios (e.g. SSP585, SSP370) lead to significantly increased likelihood of major events like those seen in the 2023–2024 fire seasons in future, highlighting the critical importance of strong climate change mitigation efforts (e.g. SSP126) to reduce the future likelihood of extreme fire events. Even though SSP370 and SSP585 scenarios suggest significant increases in extreme events, the confidence that strong mitigation (SSP126) can avoid significant portions of increased risk is a major promising outcome of this work. Our findings emphasise the importance of continued and enhanced mitigation efforts.

8.2 Roadmap for the State of Wildfires report

The report successfully achieved its primary objectives, which included identifying and contextualising extreme wildfires and wildfire seasons over the past year, selecting focal events with significant societal and environmental impacts, and diagnosing the factors contributing to these events. The report also assessed the predictive capabilities of existing systems, highlighting their strengths and limitations, and attributed the occurrence of focal events to anthropogenic factors, including climate change and land use. Additionally, it

provided an outlook for future wildfire probabilities, emphasising the limitations of current long-term forecasting tools and the increasing likelihood of extreme fire events under future climate scenarios, particularly highlighting the need for improved accuracy in regional projections.

The fire science community is currently navigating several research frontiers to improve prediction of extreme fires and understanding of their causes, with the view to enhance preparedness, response, mitigation, and adaptation to wildfires in wider society. The field is advancing its ability to observe individual fires, assess conditions leading to extreme fires, and predict their occurrence on timescales ranging from hours to decades. Additionally, there is increasing focus on monitoring and modelling the diverse impacts of extreme fires on society, the environment, and the economy.

As part of this inaugural edition of the State of Wildfires report, we present, in Appendix B, a stocktake of current capabilities, challenges, and emerging opportunities in the observation and modelling of extreme fires and their impacts. Appendix B is intended for the interdisciplinary community of fire scientists and represents a contribution to agenda-setting within this field of research. It will not be revised annually but may be revisited in future to serve as a stocktake of progress in this field. Here, we briefly summarise the specific role that the State of Wildfires report should serve as advances in the observation, prediction, and modelling of extreme fires and their impacts come to fruition.

8.2.1 Definition

The State of Wildfires report will facilitate a community
effort on a protocol for defining extreme fire events or fire
seasons.

This report emphasises important issues in the definition
of extreme fires, a problem that affects the definition of
many terms used in fire science including “wildfire” and
“megafire” (Appendix B; e.g. Shuman et al., 2022; Linley et
al., 2022). Definition is complicated by the impact of fires
on society and the environment across many impact sec-
tors, with the magnitude of impact not necessarily correlat-
ing with observable fire traits such as BA (Appendix B). In
future years, regional experts would benefit from a protocol
or guidelines that can be used for categorising extreme fire
events or seasons. To support future iterations of the State of
Wildfires report, we will coordinate workshops with broader
sections of the fire science community with the aim to pro-
duce guidance for future years. Central to this task is the in-
clusion of communities from broad geographies so that any
output respects fire impacts that are considered to be region-
ally significant.
## 8.2.2 Observation

The State of Wildfires report will advocate for and utilise
new harmonised fire observation products.

Consistent, long-term records of fire extent and properties are
fundamental for studying extremes, which cannot be charac-
terised without reference to historical ranges (Appendix B).
The MODIS instrument has been crucial in tracking global
fire progression and emissions over 2 decades, but its con-
tinuity is threatened as the Terra satellite nears decommis-
sioning, necessitating harmonisation with newer datasets like
VIIRS, Landsat, and Sentinel with MODIS records for con-
sistent fire observation.

The State of Wildfires report further underscores the crit-
ical strategic need for a continuous and harmonised dataset
of fire observations beyond the MODIS era (Appendix B).
To support future iterations of the report, we will advocate
for the provision of harmonised products within the Earth
observations communities. In addition, regional products of-
ten provide scope to characterise the extremity of events
over multidecadal timescales and are now being provided in
globally harmonised formats compatible with global analy-
ses such as ours. These regional datasets should be utilised
in future iterations of the State of Wildfires report.

The State of Wildfires report will stimulate progress on
combining multiple fire observation streams to better
identify and characterise extreme fire.

This report highlights the need to advance our capacity to ob-
serve fires that are impactful in diverse ways (Appendix B).
In particular, there is a growing need to move “beyond burned
area” and towards a wider set of intensity, severity, and be-
haviour metrics that often relate more strongly to impacts on
society and the environment than BA. The integration of in-
dividual fire data from the Global Fire Atlas in this iteration
of the State of Wildfires report is one example of including
wider fire parameters such as size and rate of growth. Fur-
ther applications of the dataset or other individual fire atlases
(e.g. Laurent et al., 2018; Artés et al., 2019) could include as-
sessing days with many synchronous large fires, which chal-
lenge fire management (e.g. Abatzoglou et al., 2021), and
identifying impactful fires by their intersection with popu-
lation centres (e.g. Modaresi Rad et al., 2023). Combining
individual fire behaviour data with fire radiative power and
biomass combustion estimates might better identify intense
or severe fires with significant consequences for ecosystems
and society (e.g. Nolan et al., 2021a).

Overall, the State of Wildfires report must stimulate
progress on moving beyond burned area and combining di-
verse observational capacities to better identify and charac-
terise extreme fire events, and we intend to expand our use of
such insights in future iterations. Likewise, the report must
be poised to adopt any emerging datasets that quantify fire

impacts on the various impact sectors outlined in Appendix
B into its definition of extreme fires.

8.2.3 Prediction

The State of Wildfires report will advocate for the use of
extended range forecast to identify early onset of fire
weather conditions.

Global fire danger monitoring systems currently use short-
to medium-range weather forecasts, typically up to 10 d
(Appendix B). However, state-of-the-art seasonal forecasting
systems can predict fire-conducive conditions up to 1 month
in advance and, in some regions, up to 2 months. Longer-
term predictability is achievable in regions where fire activity
corresponds strongly with climate modes such as ENSO.

By presenting an annual opportunity to take stock of cur-
rent capabilities in forecasting horizons for significant global
fire events, the State of Wildfires report will continue to
showcase and advocate for advances in the forecasting win-
dow for extreme fire potential on subseasonal to seasonal
timescales.

The State of Wildfires report will stimulate progress on
the use of AI and informatics methods to aid the forecast
of fire activity globally.

There is strong potential for data-driven applications, such
as machine learning, to improve predictions of extreme fire
occurrence and move beyond traditional prediction systems
based on meteorological indices such as the FWI (Appendix
B). These methods can incorporate diverse data inputs rep-
resenting the influence of fuel loads, fuel moisture, ignition
opportunities, and suppression on fire likelihood, therefore
improving upon indices that are mostly a function of weather
conditions. Tools used in this report, such as ConFire, are
structured to harness new data as they become available,
including near-real-time data, improved fuel observations,
and detailed human-fire interaction data. ConFire is also be-
ing developed to optimise its representation of extreme fire
events by incorporating more flexible response curves into
its BA predictions (Barbosa, 2024). The State of Wildfires
report will showcase the benefits of these emerging technolo-
gies in enhancing fire prediction and management.

8.2.4 Attribution

The State of Wildfires report will promote the
enhancement of low-latency attribution approaches.

Fire attribution techniques are relatively novel compared
with more established approaches for extremes such as heat-
waves. Part of the challenge in attributing fire is that it is a
complex hazard comprising multiple compound risks across
both meteorological and human drivers, all of which must
be represented in the driving data sets used by models (Ap-