In April–May 2024, unprecedented floods in Rio Grande do Sul displaced around 600,000 people and caused more than 180 deaths. This study unpacks the role of anthropogenic climate change and the preceding El Niño conditions on the extreme rainfall using a probabilistic event attribution. This event was rare even in the 2024 climate, with a return period exceeding 100 years. With global warming of 1.2 °C, such an event has become approximately 2 (0.06–4200) times as likely, or equivalently 12% (−13 to +43%) more intense. The recent El Niño event also approximately doubled (0.7–37 times) the likelihood of such an event relative to a neutral year. In any disaster, the vulnerability and exposure context play a crucial role in turning the meteorological hazard into impacts, underscoring the need for equitable adaptation measures to break the cycle of risk and inequality in the context of a warming climate.

The 2025 European fire season was historically extreme, with record-breaking burned area exceeding 1 400 000 ha, and multiple regionally unprecedented wildfires. Emerging fire regimes and extreme wildfire behaviour in Europe pose increasing adaptation challenges. Extreme event attribution of a recent fire season, combined with analysis of changes in vegetation and land use, provides insight into the effect of climate and environmental change on high impact events. We analyse five regions that experienced particularly extreme wildfire activity in 2025 (northwestern Iberia, western and northern Britain, Occitania, the eastern Adriatic/Ionian, and northern and western Türkiye) capturing a diverse range of driving weather conditions and fire regimes. Strong trends towards drier summers and extreme weekly vapour pressure deficit (VPD) were found, with summer drought emergent from natural variability in most southern European regions, and VPD extreme emergent in reanalysis data for all regions. Changes in VPD are the main reason why combined hot, dry, and windy conditions have become more frequent than expected from natural variability. This emergence is seen in both reanalysis data and climate models for the Iberian, Adriatic/Ionian and Turkish regions. In contrast, in the British and Occitanian regions, models do not show observed trends.

In April 2023, India, Bangladesh, Thailand and Lao People’s Democratic Republic (PDR) were hit by ‘humid’ heatwaves. Using peer-reviewed attribution methods, we analyse the role of human-induced climate change in altering the likelihood and magnitude of this event, over two domains- (i) southern India (excluding the semi-arid regions to the lee of the Western Ghats) and Bangladesh (hereafter, IB) and (ii) Thailand and Lao PDR (hereafter, TL). Using Heat Index (HI), a metric that reflects the ‘feels-like’ air temperature due to humidity, the events are defined as the maximum of 4-day mean HI in April (HIx4d), area-averaged over the respective domains. In the observed records, such events are not exceptional over IB in today’s climate (1-in-5-year event), but rare over TL (1-in-200-year event). By including climate models, a heatwave as extreme as the 2023 event in IB is found to be made at least 30 times more likely by human-induced climate change and 2°C hotter than a 1-in-5-year event in the pre-industrial climate. Over TL, the observed HI would have been virtually impossible to occur in the pre-industrial climate and is now 2.3°C hotter. If global warming continues, similar heatwaves are expected to become even more frequent- about 10 times more likely in TL and about 3 times more likely in IB in a 2°C warmer world, as compared to the 2023 climate. Although accustomed to humid heat, many in these regions face heightened heat-related health risks due to physiological vulnerability, occupational exposure, and societal disadvantages. Environmental factors like air pollution and urban heat effects further worsen impacts, especially for the most vulnerable. Strengthening early warning systems, improving vulnerability assessments, and ensuring inclusive, locally tailored heat action measures are critical to reducing future heat-related risks across the region.

Heatwaves increasingly threaten public health in the Mediterranean region, and Greece is among the hardest hit countries. Yet evidence on long-term adaptation, spatial vulnerability, and the contribution of human-induced climate change to heatwave-related mortality in Greece remains limited. We analysed 2,144,957 all-cause deaths in Greece (2000–2019) in people older than 65 years using a time-stratified case-crossover design. We derived population-weighted daily maximum temperatures at NUTS3 level from ERA5 reanalysis and WorldPop. We applied six heatwave definitions (HD1–HD6) varying by duration (≥ 2 or ≥ 3 days) and thresholds (90th, 95th, 99th percentiles). We fitted Bayesian hierarchical Poisson models to estimate heatwave-mortality associations varying by space and time. We additionally adjusted for relative humidity and national holidays. We then combined these estimates with probabilistic climate-attribution methods to quantify the number and proportion of heatwave-related deaths attributable to human-induced climate change. Heatwaves raised mortality consistently, with relative risks from 1.08 (95

From October 2020 to early 2023, Eastern Africa experienced five consecutive failed (SPEI -2.6) rainy seasons, resulting in the worst drought in 40 years. This led to harvest failures, livestock losses, water scarcity, and conflicts, leaving approximately 4.35 million people in need of humanitarian aid. To understand the role of human-induced climate change in the drought, we analysed rainfall trends and the combined effect of rainfall deficit with high temperatures in the Southern Horn of Africa covering parts of southern Ethiopia, southern Somalia, and eastern Kenya. We employed various climate models and observations to assess changes in 24-month rainfall (2021–2022), and seasonal rainfall; both the (March-April-May, MAM) ‘long rains’ and (October-November-December, OND) ‘short rains’ in 2022. We also contextualised the event in terms of vulnerability and exposure to understand how these elements influenced the magnitude of the impacts. Our analysis shows that anthropogenic influence on the combined effects of low rainfall and high evapotranspiration caused by higher temperatures made the drought exceptional, leading to major crop and pasture losses and water shortages. Our results also show a decline in rainfall during MAM and an upward trend during OND, which is attributable to climate change. Despite the wetting trend in OND season, the drought years concluded with successive La Niña conditions, typically linked with below-average rainfall in the region during that season. We do not find a trend in the 24-month precipitation. The assessment on vulnerability and exposure highlights the need for enhanced preparedness of government drought management systems and international aid infrastructure for future severe and prolonged droughts. The study's findings, combined with climate projections that indicate increased heavy precipitation in the region, underscore the pressing necessity for robust adaptation strategies that can address both wet and dry extremes. The impacts of climate change in Eastern Africa necessitate investments in adaptive measures and resilience building that can evolve with emerging climate signals.

Canada’s 2023 wildfire season was the most extreme on record, with almost 15 million hectares burned—more than double the previous record. We use an established attribution protocol to examine seasonal and regional changes in weather-related wildfire risk associated with global warming, and also evaluate the extent to which 2023’s unusual level of blocking activity contributed to the severity of the season. We find that the annual accumulated daily severity rating (DSR), a measure of weather-related fire risk) is increasing in most ecozones in response to global warming, with the largest increases in the early months of the fire season; although temperatures are increasing everywhere, this effect is offset in some regions by increased precipitation. Blocking circulation patterns are likewise associated with increased DSR, with the strongest responses in May and September. However, there is wide regional variability, illustrated through two case studies of regions that experienced particularly intense wildfires. In the southern Taiga Plains, the contribution from anthropogenic climate change is unclear, while blocking activity increased the severity of the season by at least 33%; in the East James Bay region, the season was found to be at least 32% more intense due to global warming, and a further 15% more intense due to blocking activity.

In the 2022 summer, western–central Europe and several other regions in the northern extratropics experienced substantial soil moisture deficits in the wake of precipitation shortages and elevated temperatures. Much of Europe has not witnessed a more severe soil drought since at least the mid-20th century, raising the question whether this is a manifestation of our warming climate. Here, we employ a well-established statistical approach to attribute the low 2022 summer soil moisture to human-induced climate change using observation-driven soil moisture estimates and climate models. We find that in western–central Europe, a June–August root zone soil moisture drought such as in 2022 is expected to occur once in 20 years in the present climate but would have occurred only about once per century during preindustrial times. The entire northern extratropics show an even stronger global warming imprint with a 20-fold soil drought probability increase or higher, but we note that the underlying uncertainty is large. Reasons are manifold but include the lack of direct soil moisture observations at the required spatiotemporal scales, the limitations of remotely sensed estimates, and the resulting need to simulate soil moisture with land surface models driven by meteorological data. Nevertheless, observation-based products indicate long-term declining summer soil moisture for both regions, and this tendency is likely fueled by regional warming, while no clear trends emerge for precipitation. Finally, our climate model analysis suggests that under 2 ∘C global warming, 2022-like soil drought conditions would become twice as likely for western–central Europe compared to today and would take place nearly every year across the northern extratropics.

Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure, and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions and to understand whether an extremely cold winter, such as the coldest winter on the historical record of Germany in 1963 (−6.3 °C or −3.4σ seasonal December–January–February (DJF) temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as the second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): a winter as cold as 1963 is still physically possible in central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extremely cold winter to avoid potential maladaptation and increased vulnerability.

To investigate the extent to which differences in regional model projections can be explained by differences in the warming rates of their driving models, we compare projections of temperature and precipitation over the UK from two regional climate ensembles-the EuroCORDEX multi-model ensemble and UKCP18 perturbed parameter ensemble-along with projections produced by the "parent" GCMs from which boundary conditions were taken. We evaluate the ensembles in terms of their representation of recent climate, then compare the changes simulated between 1981-2010 and 2050-2079. While both ensembles exhibit seasonal biases with similar magnitudes and spatial patterns during the evaluation period, the UKCP18 ensemble exhibits a somewhat stronger change signal in future simulations, due to a combination of higher climate sensitivity of the driving models, variations in the forcings applied, and-in the regional simulations-the inclusion of time-varying aerosols. In order to reconcile the two sets of projections, we compare two periods corresponding to fixed global warming levels in the driving models, to constrain the variability within and between the ensembles which can be ascribed to differing rates of global warming: the discrepancy between the ensembles is greatly reduced, although some differences in the local response remain, with the UKCP18 runs slightly warmer and drier than the EuroCORDEX runs, particularly in summer. We also highlight potential pitfalls of comparing warming levels with a reference time period, due to uncertainty about the warming that has already occurred in the driving models prior to the reference period. We compare temperature and precipitation over the UK from two different collections (known as "ensembles") of climate model runs: the EuroCORDEX ensemble, consisting of simulations from many combinations of global- and regional-scale models; and the UKCP18 regional ensemble, which uses a single pair of models, but adjusts the model parameters for each run. Both ensembles perform well in the current climate, but future changes in the UKCP18 ensemble are generally larger by 2050-2079 than those in the EuroCORDEX ensemble. This is largely because the UKCP18 global models warm more quickly in response to the greenhouse gases in the atmosphere, and use slightly higher concentrations of greenhouse gases. To understand the differences between the two ensembles that cannot be explained by differences in the rate of global warming, we also look at changes as the models warm from 1 to 2 degrees C globally above levels in the early 20th century. This reduces the discrepancy between the ensembles, although some differences remain: the UKCP18 ensemble remains slightly warmer and drier than EuroCORDEX, particularly in summer. We highlight issues that arise when comparing simulations at a given warming level against simulations in a fixed decade, due to uncertainty about how much warming has already occurred. The UKCP and EuroCORDEX regional model ensembles have similar biases, but project very different future climate over the UKThese differences are driven largely by differences in the climate sensitivity of the GCMs used to force the regional modelsComparing projections after a specified degree of warming, rather than in specified decades, reduces but does not resolve these differences

Heavy rainfall in eastern Africa between late 2019 and mid 2020 caused devastating floods and landslides throughout the region. These rains drove the levels of Lake Victoria to a record-breaking maximum in the second half of May 2020. The combination of high lake levels, consequent shoreline flooding, and flooding of tributary rivers caused hundreds of casualties and damage to housing, agriculture, and infrastructure in the riparian countries of Uganda, Kenya, and Tanzania. Media and government reports linked the heavy precipitation and floods to anthropogenic climate change, but a formal scientific attribution study has not been carried out so far. In this study, we characterize the spatial extent and impacts of the floods in the Lake Victoria basin and then investigate to what extent human-induced climate change influenced the probability and magnitude of the record-breaking lake levels and associated flooding by applying a multi-model extreme event attribution methodology. Using remote-sensing-based flood mapping tools, we find that more than 29 000 people living within a 50 km radius of the lake shorelines were affected by floods between April and July 2020. Precipitation in the basin was the highest recorded in at least 3 decades, causing lake levels to rise by 1.21 m between late 2019 and mid 2020. The flood, defined as a 6-month rise in lake levels as extreme as that observed in the lead-up to May 2020, is estimated to be a 63-year event in the current climate. Based on observations and climate model simulations, the best estimate is that the event has become more likely by a factor of 1.8 in the current climate compared to a pre-industrial climate and that in the absence of anthropogenic climate change an event with the same return period would have led lake levels to rise by 7 cm less than observed. Nonetheless, uncertainties in the attribution statement are relatively large due to large natural variability and include the possibility of no observed attributable change in the probability of the event (probability ratio, 95 % confidence interval 0.8–15.8) or in the magnitude of lake level rise during an event with the same return period (magnitude change, 95 % confidence interval 0–14 cm). In addition to anthropogenic climate change, other possible drivers of the floods and their impacts include human land and water management, the exposure and vulnerability of settlements and economic activities located in flood-prone areas, and modes of climate variability that modulate seasonal precipitation. The attribution statement could be strengthened by using a larger number of climate model simulations, as well as by quantitatively accounting for non-meteorological drivers of the flood and potential unforced modes of climate variability. By disentangling the role of anthropogenic climate change and natural variability in the high-impact 2020 floods in the Lake Victoria basin, this paper contributes to a better understanding of changing hydrometeorological extremes in eastern Africa and the African Great Lakes region.

The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community.

This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of “intermodel empirical orthogonal function” analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom.

Heat extremes have been increasing both in frequency and in intensity in most land regions of the world, and this increase has been attributed to human activities. In the last decade, many outstanding and record shattering heat extremes have occurred worldwide, triggering fears of a nonlinear behaviour or an 'acceleration' in the development of heat conditions, considering the warming level when the event occurred. Here we show that the evolution of yearly temperature maxima, with return periods (RPs) above 10 years, consistently shifts with global temperatures and does not significantly depart from this behaviour in recent years or decades when considered globally or at the scale of continents. This result is obtained by using a classical statistical event attribution technique, where the assumption that the distribution of block-maxima extremes linearly shifts with global warming is tested across years and world land regions. However, the pace of frequency change is large, with the probability of heat extremes exponentially rising and nearly doubling every decade since 1979, particularly when considering events with a RP of about 10-50 years in 2000. This makes the climate of a decade ago unrepresentative of today's climate. Our results overall mean that we do not expect events like the recent outstanding extremes to undergo nonlinear changes, despite fast changes. They also show that assumptions underlying attribution techniques used in many recent studies are consistent with recent temperature trends.

As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

Abstract This chapter describes new methods and datasets, developed through UK Climate Resilience Programme (UKCR) projects, to better understand climate hazards. We describe projections of hazards using new tools and provide examples of applications for decision-making. Going forward, this new physical and statistical understanding should be incorporated into climate risk assessments.

Input data used in Pietroiusti et al. 2023 (submitted), for the water balance model and analysis scripts. Publicly available netcdf data used in the analysis is not included in this repository, but links to the datasets are detailed in the manuscript. Any additional data is available by request to the author.

Abstract. Central European winters have warmed markedly since the mid-20th century. Yet cold winters are still associated with severe societal impacts on energy systems, infrastructure and public health. It is therefore crucial to anticipate storylines of worst-case cold winter conditions, and to understand whether an extremely cold winter, such as the coldest winter in the historical record of Germany in 1963 (−6.3 °C or −3.4σ seasonal DJF temperature anomaly relative to 1981–2010), is still possible in a warming climate. Here, we first show based on multiple attribution methods that a winter of similar circulation conditions to 1963 would still lead to an extreme seasonal cold anomaly of about −4.9 to −4.7 °C (best estimates across methods) under present-day climate. This would rank as second-coldest winter in the last 75 years. Second, we conceive storylines of worst-case cold winter conditions based on two independent rare event sampling methods (climate model boosting and empirical importance sampling): winter as cold as 1963 is still physically possible in Central Europe today, albeit very unlikely. While cold winter hazards become less frequent and less intense in a warming climate overall, it remains crucial to anticipate the possibility of an extreme cold winter to avoid potential maladaptation and increased vulnerability.