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

Understanding precipitation properties at regional scales and generating reliable future projections is crucial in providing actionable information for decision-makers, especially in regions with high vulnerability to climate change, where future changes impact ecosystem resilience, biodiversity, agriculture, water resources and human health. The South America Convection-Permitting Regional Climate Model experiment (SA-CPRCM) examines climate change effects in convection-permitting simulations at 4.5 km resolution, on climate time scales (10 year present-day and 10-year future RCP8.5 around 2100), over a domain covering most of South America, using the Met Office Unified Model (UM) convection-permitting RCM. Under the RCP8.5 scenario, precipitation in the CPRCM decreases, becomes less frequent and more seasonal over the Eastern Amazon region. Dry spells lengthen, increasing the risk of drought. In the Western Amazon, precipitation increases in the wetter austral autumn (Apr. – Jun.) and decreases in the drier austral winter and spring (July – Oct.), leading to a more distinct dry season and imposing a greater risk of contraction of the tropical forest. Over South-eastern Brazil, future precipitation increases and becomes more frequent and more intense, increasing the risk of floods and landslides. A future increase in the intensity of precipitation and extremes is evident over all these regions, regardless of whether the mean precipitation is increasing or decreasing. The CPRCM and its driving GCM respond in a similar way to the future forcing. The models produce broadly similar large-scale spatial patterns of mean precipitation and comparable changes to frequency, intensity, and extremes, although the magnitude of change varies by region and season. [ABSTRACT FROM AUTHOR]

Pembrolizumab has received approval in the UK as first‐line monotherapy for recurrent and/or metastatic HNSCC (R/M HNSCC) following the results of the KEYNOTE‐048 trial, which demonstrated a longer overall survival (OS) in comparison to the EXTREME chemotherapy regimen in patients with a combined positive score (CPS) ≥1. In this article, we provide retrospective real‐world data on the role of pembrolizumab monotherapy as first‐line systemic therapy for HNSCC across 18 centers in the UK from March 20, 2020 to May 31, 2021. 211 patients were included, and in the efficacy analysis, the objective response rate (ORR) was 24.7%, the median progression‐free survival (PFS) was 4.8 months (95% confidence interval [CI]: 3.6–6.1), and the median OS was 10.8 months (95% CI 9.0–12.5). Pembrolizumab monotherapy was well tolerated, with 18 patients having to stop treatment owing to immune‐related adverse events (irAEs). 53 patients proceeded to second‐line treatment with a median PFS2 of 10.2 months (95% CI: 8.8–11.5). Moreover, patients with documented irAEs had a statistically significant longer median PFS (11.3 vs. 3.3 months; log‐rank p value = <.001) and median OS (18.8 vs. 8.9 months; log‐rank p value <.001). The efficacy and safety of pembrolizumab first‐line monotherapy for HNSCC has been validated using real‐world data. [ABSTRACT FROM AUTHOR]

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

Drought events have increased in frequency and severity in recent years, and result in significant economic losses. Although the Brazilian semi-arid northeast has been historically associated with the impacts of drought, drought is of national concern, from 2011–2019, drought events were recorded in all Brazilian territories. Droughts can have major consequences for agricultural production, which is of particular concern given the importance of soybeans for socio-economic development. Due to its regional heterogeneity, it is important to develop accurate drought forecast and assessment tools for Brazil. We explore machine learning as a method to forecast the vegetation health index (VHI), for large scale monthly drought monitoring across agricultural land in Brazil. Furthermore, we also determine spatio-temporal drivers of VHI across the wide variation in climates, as well as evaluate machine learning performance for ENSO variation, forecasting of the onset of drought impact, and how the trade off between spatial variation and sample size affects model performance. We show that machine learning methods such as gradient boosting methods are able to more easily forecast vegetation health in the north and north east Brazil than south Brazil, and perform better during La Niña events than El Niño events. Drought impacts which reduce VHI below the commonly used 40 % threshold can be forecast across Brazil with similar model performance. SPEI is shown to be a useful indicator of drought impact, with 3 month accumulation periods preferred over 1 and 2 months. Results aim to inform future developments in operational drought monitoring at the National Center for Monitoring and Early Warning of Natural Disasters in Brazil (CEMADEN). Future work should build upon methods discussed here to improve drought forecasts for agricultural drought response and adaptation. [ABSTRACT FROM AUTHOR]