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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. [ABSTRACT FROM AUTHOR]

Compound wind and precipitation extremes (CWPEs) can severely impact natural and socioeconomic systems. However, our understanding of CWPE future changes, drivers, and uncertainties under a warmer climate is limited. Here, by analyzing the event both on oceans and landmasses via state‐of‐the‐art climate model simulations, we reveal a poleward shift of CWPE occurrences by the late 21st century, with notable increases at latitudes exceeding 50° in both hemispheres and decreases in the subtropics around 25°. CWPE intensification occurs across approximately 90% of global landmasses, and is most pronounced under a high‐emission scenario. Most changes in CWPE frequency and intensity (about 70% and 80%, respectively) stem from changes in precipitation extremes. We further identify large uncertainties in CWPE changes, which can be understood at the regional level by considering climate model differences in trends of CWPE drivers. These results provide insights into understanding CWPE changes under a warmer climate, aiding robust regional adaptation strategy development. Plain Language Summary: Concurrent wind and precipitation extremes (CWPEs), a typical case of compound weather events, can cause flooding and strong winds that can paralyze public transportation, trigger power outages, and destroy houses and shelters. Furthermore, CWPE over the ocean can endanger the shipment of goods and its logistics. Yet, our understanding of the projected changes, underlying drivers, and uncertainties under a warmer climate is limited. Here, analyzing for the first time CWPEs both on global oceans and landmasses allows us to reveal a poleward shift of CWPEs at the global scale in response to climate change. We show that changes in precipitation extremes play a dominant role in determining the future changes in the frequency and intensity of CWPEs. Furthermore, at the regional level, we reveal substantial uncertainties in projections due to differences between the used climate models. We illustrate that these uncertainties are due to model differences in trends of CWPE drivers and argue that they should be addressed explicitly in impact assessments for guiding the development of robust adaptation strategies. Key Points: An intensification and poleward shift of compound wind and precipitation extremes (CWPEs) will occur in a warmer climateMost changes in frequency and intensity of CWPEs stem from changes in precipitation extremesSubstantial uncertainties at the regional level in projections of CWPEs are due to structural model differences [ABSTRACT FROM AUTHOR]

Climate impact models often require unbiased point‐scale observations, but climate models typically provide biased simulations at the grid scale. While standard bias adjustment methods have shown to generally perform well at adjusting climate model biases, they cannot overcome the gap between grid‐box and point scale. To overcome this limitation, combined bias adjustment and stochastic downscaling methods have been developed. These methods, however, are single‐site methods and cannot represent spatial dependence. Here we propose a multisite stochastic downscaling method that can be applied to bias‐adjusted climate model output for generating spatially coherent time series of daily precipitation at multiple stations, conditional on the driving climate model. The method is based on a transformed truncated multivariate Gaussian model and can also be used to downscale to a full field at finer‐grid resolution. An evaluation for stations across selected catchments in Austria demonstrates the good performance of the stochastic model at representing marginal, temporal and spatial aspects of daily precipitation, including extreme events. [ABSTRACT FROM AUTHOR]

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes. • Changes in precipitable water are the baseline for changes in extreme precipitation. • Changes in extreme precipitation are modulated by changes in vertical velocity. • Changes in driver dependence have minor impact on extreme precipitation changes. [ABSTRACT FROM AUTHOR]

Long-duration dry spells in combination with temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a 15 consecutive number of days with daily precipitation less than 1mm. CMIP5 models tend to underestimate the persistence of dry spells in Northern Europe while a large variability exists between model estimates in Central and Southern Europe where models have contrasting biases. Our results indicate that this variability in model estimates is due to inherent model differences and not internal variability. In Northern Europe, differences in the representation of persistent dry spells are related to the representation of persistent anticyclonic conditions. We also 20 find a large spread in the representation of temperature extremes during dry spells. In Central and Southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Overall, there are large discrepancies in the representation of long-duration dry and hot events in the CMIP5 ensemble where the simulated climates vary from models with shorter-cooler dry spells to models with longer-hotter dry spells. This information 25 is important to consider when interpreting the plausibility of future projections from climate models. [ABSTRACT FROM AUTHOR]

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (a) preconditioned, (b) multivariate, (c) temporally compounding, and (d) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (a) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (b) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (c) In Portugal, deeplandslides are often caused by temporal clusters of moderate precipitation events. Finally, (d) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors. [ABSTRACT FROM AUTHOR]

Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias-assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard indicators: the wet-bulb globe temperature (WBGT) and the Chandler burning index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazard indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 ∘C) due to the biases driven by relative humidity (20 ∘C). Biases in WBGT (1.1 ∘C) are small compared to the biases driven by temperature (1.9 ∘C) and relative humidity (1.4 ∘C), as the two biases compensate for each other. In many regions, also biases related to the statistical dependence (0.85 ∘C) are important for WBGT, which indicates that well-designed physically based multivariate bias adjustment procedures should be considered for hazards and impacts that depend on multiple drivers. The proposed compound-event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases' sources in the simulation of climate impacts. [ABSTRACT FROM AUTHOR]

The future climate projections in the IPCC reports are visually communicated via maps showing the mean response of climate models to alternative scenarios of socio‐economic development. The presence of large changes is highlighted by stippling the maps where the mean climate response (the signal) is large compared to internal variability (the noise) and the response is robust, that is, consistent in sign, across the individual models. In addition, hatching is used to mark the regions with a small multi‐model mean change. This approach may fail to recognize the risk of large changes in regions where the uncertainty is large and the response is not robust. Here, we present a more informative diagnostic to support risk assessment that is obtained by quantifying the mean forced signal‐to‐noise ratio of the individual model responses, rather than the signal‐to‐noise ratio of the mean response. This enables us to identify regions where a large future change compared to year‐to‐year variability is plausible, regardless of whether the signal is robust across the ensemble. For mean precipitation changes, we find that the majority (58% in surface area) of the unmarked regions and a sizeable portion (19%) of the hatched regions from the AR5 projections hid climate change responses to the RCP8.5 scenario that are on average large compared to the year‐to‐year variability. Based on the newer CMIP6 ensemble, a considerable potential for large annual‐mean precipitation changes, despite the lack of a robust multi‐model projection, exists over 22% of the surface land area, particularly in Central America, northern South America (including the Amazon), Central and West Africa (including parts of the Sahel), and the Maritime Continent. [ABSTRACT FROM AUTHOR]

The simultaneous occurrence of extremely wet winters at multiple locations in the same region can contribute to widespread flooding and associated socio‐economic losses. However, the spatial extent of precipitation extremes (i.e., the area in which nearby locations experience precipitation extremes simultaneously) and its future changes are largely overlooked in climate assessments. Employing new multi‐thousand‐year climate model simulations, we show that under both 2.0 °C and 1.5 °C warming scenarios, wintertime total precipitation extreme extents would increase over about 80%–90% of the Northern Hemisphere extratropics (i.e., of the latitude band 28°–78°N). Stabilizing at 1.5 °C rather than 2.0 °C would reduce the average magnitude of the increase by 1.7–2 times. According to the climate model, the increased extents are caused by increases in precipitation intensity rather than changes in the spatial organization of the events. Relatively small percentage increases in precipitation intensities (e.g., by 4%) can drive disproportionately larger, by 1–2 orders of magnitude, growth in the spatial extents (by 93%). Plain Language Summary: One of the most impact‐relevant and studied effects of global warming is the intensification of precipitation extremes. When extremely wet winters occur simultaneously at multiple locations within the same region, their cumulative impacts can be particularly high and enhanced as a result of limited resources available to cope with simultaneous damages. Despite the impacts caused by widespread extremes, climate change studies have typically disregarded the spatial extension of the extremes. Here, based on new multi‐thousand‐year climate model simulations, we show that—over most of the Northern Hemisphere extratropics, that is, most of the latitude band 28°–78°N—the spatial footprint of total wintertime precipitation extremes is projected to largely widen in the future. This widening results from a warmer, and therefore moister, atmosphere that will intensify precipitation. Holding global warming to 1.5 °C rather than 2.0 °C, in line with the Paris Agreement, would be beneficial to society as it could limit the average increase in the extension over the Northern Hemisphere extratropics by up to a factor of 2. To develop better preparedness for such extreme events, stakeholders should consider that a small increase in precipitation intensities (for example, by 4%) could result in large (by 93%) increases in spatial extent. Key Points: The spatial extent of wintertime total precipitation extremes is projected to increase over most of the Northern Hemisphere in the futureChanges in the spatial organization, that is, dependence, of precipitation extremes only marginally affect the spatial extentsIncreased precipitation magnitudes can cause a disproportionate (even 20–90 times larger) increase in the precipitation extreme extents [ABSTRACT FROM AUTHOR]

Climate models' outputs are affected by biases that need to be detected and adjusted to model climate impacts. Many climate hazards and climate-related impacts are associated with the interaction between multiple drivers, i.e. by compound events. So far climate model biases are typically assessed based on the hazard of interest, and it is unclear how much a potential bias in the dependence of the hazard drivers contributes to the overall bias and how the biases in the drivers interact. Here, based on copula theory, we develop a multivariate bias assessment framework, which allows for disentangling the biases in hazard indicators in terms of the underlying univariate drivers and their statistical dependence. Based on this framework, we dissect biases in fire and heat stress hazards in a suite of global climate models by considering two simplified hazard indicators, the wet-bulb globe temperature (WBGT) and the Chandler Burning Index (CBI). Both indices solely rely on temperature and relative humidity. The spatial pattern of the hazards indicators is well represented by climate models. However, substantial biases exist in the representation of extreme conditions, especially in the CBI (spatial average of absolute bias: 21 °C) due to the biases driven by relative humidity (20 °C). Biases in WBGT (1.1 °C) are small compared to the biases driven by temperature (1.9 °C) and relative humidity (1.4 °C), as the two biases compensate each other. In many regions, also biases related to the statistical dependence (0.85 °C) are important for WBGT, which indicates that well-designed physically-based multivariate bias adjustment should be considered for hazards and impacts that depend on multiple drivers. The proposed compound event-oriented evaluation of climate model biases is easily applicable to other hazard types. Furthermore, it can contribute to improved present and future risk assessments through increasing our understanding of the biases' sources in the simulation of climate impacts. [ABSTRACT FROM AUTHOR]

Previous studies indicate atmospheric instability, total column water vapor, and horizontal moisture transport as major drivers of precipitation extremes, however little is known about how the combination of these drivers affects precipitation extremes across the world. Here, using daily data from the ERA-5 reanalysis spanning the period 1981–2020, we identified the combinations of extreme values for these three major drivers that enhance the probability of daily precipitation extremes on a global scale. Our findings show that extreme daily precipitation is practically impossible without any of these drivers being extreme. Atmospheric instability is the primary driver of precipitation extremes, meaning that, among the three cases of the drivers being extreme in isolation, extreme atmospheric instability is associated with the highest average probability of extreme precipitation over landmasses (29% during December–February, 32% during June–August). When considering the combination of two drivers being simultaneously extreme, joint extremes of atmospheric instability and total column water vapor (and non-extreme horizontal moisture transport) lead to the highest probability of extreme precipitation (69% during December–February, 70% during June–August), which is similar to the probability under three drivers in extreme conditions (67% and 72%). Our results point to a latitudinal variation of the combination that leads to the highest probability of extreme precipitation. In subtropics, the case of the three extreme drivers dominates, whereas in extratropical regions, the dominant combination is that of the joint extremes of atmospheric instability and total column water vapor (and non-extreme horizontal moisture transport). By providing information on the most important drivers of precipitation extremes worldwide, these results can serve as a basis for evaluating precipitation extremes in climate models and understanding projected changes, which is vital for developing robust risk assessments. • The dominant combination of three drivers of extreme precipitation is analyzed. • Extreme values of at least one driver are needed for extreme precipitation. • One combination of only two extreme drivers is the most relevant in many regions. • The most influential combination varies according to latitude. [ABSTRACT FROM AUTHOR]