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The response of precipitation extremes to climate change is considered using results from theory, modeling, and observations, with a focus on the physical factors that control the response. Observations and simulations with climate models show that precipitation extremes intensify in response to a warming climate. However, the sensitivity of precipitation extremes to warming remains uncertain when convection is important, and it may be higher in the tropics than the extratropics. Several physical contributions govern the response of precipitation extremes. The thermodynamic contribution is robust and well understood, but theoretical understanding of the microphysical and dynamical contributions is still being developed. Orographic precipitation extremes and snowfall extremes respond differently from other precipitation extremes and require particular attention. Outstanding research challenges include the influence of mesoscale convective organization, the dependence on the duration considered, and the need to better constrain the sensitivity of tropical precipitation extremes to warming.

Proposed ways of improving adaptation to climate change have most often been supported by narrowly framed and separate analysis. This article investigates how different levels of vulnerability and resilience interplay with adaptation to extreme temperatures, what is the nature of these relationships and whether lower vulnerability and higher resilience contribute to increased adaptation. This article explores the governance implications of a project that, unlike other, brings together vulnerability, resilience and adaptation assessments. The project has made significant advances in addressing the current deficit integrated assessments for shaping governance propositions. Such propositions argue that the diverse levels of vulnerability and resilience convey important bases for (1) targeting at-risk older individuals; (2) developing vulnerability reduction actions; (3) resilience building actions; and (4) understanding ‘success cases’ and learn from them for developing appropriate policy measures. Taken together, these propositions offer a social, psychological and health framework not simply for governing extreme temperatures but for governing responses to climate change at large.

Reviewing recent literature, we report that changes in extreme heat event characteristics such as magnitude, frequency, and duration are highly sensitive to changes in mean global-scale warming. Numerous studies have detected significant changes in the observed occurrence of extreme heat events, irrespective of how such events are defined. Further, a number of these studies have attributed present-day changes in the risk of individual heat events and the documented global-scale increase in such events to anthropogenic-driven warming. Advances in process-based studies of heat events have focused on the proximate land-atmosphere interactions through soil moisture anomalies, and changes in occurrence of the underlying atmospheric circulation associated with heat events in the midlatitudes. While evidence for a number of hypotheses remains limited, climate change nevertheless points to tail risks of possible changes in heat extremes that could exceed estimates generated from model outputs of mean temperature. We also explore risks associated with compound extreme events and nonlinear impacts associated with extreme heat.

Robust evidence exists that certain extreme weather and climate events, especially daily temperature and precipitation extremes, have changed in regard to intensity and frequency over recent decades. These changes have been linked to human-induced climate change, while the degree to which climate change impacts an individual extreme climate event (ECE) is more difficult to quantify. Rapid progress in event attribution has recently been made through improved understanding of observed and simulated climate variability, methods for event attribution and advances in numerical modelling. Attribution for extreme temperature events is stronger compared with other event types, notably those related to the hydrological cycle. Recent advances in the understanding of ECEs, both in observations and their representation in state-of-the-art climate models, open new opportunities for assessing their effect on human and natural systems. Improved spatial resolution in global climate models and advances in statistical and dynamical downscaling now provide climatic information at appropriate spatial and temporal scales. Together with the continued development of Earth System Models that simulate biogeochemical cycles and interactions with the biosphere at increasing complexity, these make it possible to develop a mechanistic understanding of how ECEs affect biological processes, ecosystem functioning and adaptation capabilities. Limitations in the observational network, both for physical climate system parameters and even more so for long-term ecological monitoring, have hampered progress in understanding bio-physical interactions across a range of scales. New opportunities for assessing how ECEs modulate ecosystem structure and functioning arise from better scientific understanding of ECEs coupled with technological advances in observing systems and instrumentation. This article is part of the themed issue ‘Behavioural, ecological and evolutionary responses to extreme climatic events’.

Extreme precipitation trends and events are fundamental for the definition of the region’s climate and allow the subsequent analysis of the risk for the territory and the possible countermeasures. This study takes into account the Marche Region (Central Italy) with 128 rain gauges from 1921 to 2017. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. Initially, in order to obtain a rainfall overview, the dominant trend of the period 1921–2017 was evaluated. In addition, to obtain a comparable analysis, the average precipitations grouped in climatological standard normals were analyzed. Finally, the main purpose of the research was achieved by analyzing extreme events in the middle Adriatic side. In addition, forecasts of extreme precipitation events, with a return period of 100 years, were made using the theory of “generalized extreme value” (GEV). The innovation of this research is represented by the use of geostatistics to spatialize the variables investigated, through a clear and immediate graphic representation performed through GIS software. This study is a necessary starting point for the study of climate dynamics in the region, and it is also a useful tool for land use planning.

This review article provides a synthesis and perspective on how weather and climate extreme events can play a role in influencing tipping elements and triggering tipping points in the Earth System. An example of a potential critical global tipping point, induced by climate extremes in an increasingly warmer climate, is Amazon rainforest dieback that could be driven by regional increases in droughts and exacerbated by fires, in addition to deforestation. A tipping element associated with the boreal forest might also be vulnerable to heat, drought and fire. An oceanic example is the potential collapse of the Atlantic meridional overturning circulation due to extreme variability in freshwater inputs, while marine heatwaves and high acidity extremes can lead to coral reef collapse. Extreme heat events may furthermore play an important role in ice sheet, glacier and permafrost stability. Regional severe extreme events could also lead to tipping in ecosystems, as well as in human systems, in response to climate drivers. However, substantial scientific uncertainty remains on mechanistic links between extreme events and tipping points. Earth observations are of high relevance to evaluate and constrain those links between extreme events and tipping elements, by determining conditions leading to delayed recovery with a potential for tipping in the atmosphere, on land, in vegetation, and in the ocean. In the subsurface ocean, there is a lack of consistent, synoptic and high frequency observations of changes in both ocean physics and biogeochemistry. This review article shows the importance of considering the interface between extreme events and tipping points, two topics usually addressed in isolation, and the need for continued monitoring to observe early warning signs and to evaluate Earth system response to extreme events as well as improving model skill in simulating extremes, compound extremes and tipping elements.

The workshop, conducted virtually due to travel restrictions related to COVID-19, gathered scientists from six countries and focused on the mechanistic understanding, statistical characterization, and modeling of societally relevant compound climate events and extremes in the midlatitudes. These ranged from co-occurring hot–humid or wet–windy extremes, to spatially compounding wet and dry extremes, to temporallycompounding hot–wet events and more. The aim was to bring together selected experts studying a diverse range of compound climate events and extremes to present their ongoing work and outline challenges and future developments in this societally relevant field of research.

The extent to which a given extreme weather or climate event is attributable to anthropogenic climate change is a question of considerable public interest. From a scientific perspective, the question can be framed in various ways, and the answer depends very much on the framing. One such framing is a risk-based approach, which answers the question probabilistically, in terms of a change in likelihood of a class of event similar to the one in question, and natural variability is treated as noise. A rather different framing is a storyline approach, which examines the role of the various factors contributing to the event as it unfolded, including the anomalous aspects of natural variability, and answers the question deterministically. It is argued that these two apparently irreconcilable approaches can be viewed within a common framework, where the most useful level of conditioning will depend on the question being asked and the uncertainties involved.

Numerical weather prediction models require ever‐growing computing time and resources but, still, have sometimes difficulties with predicting weather extremes. We introduce a data‐driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern‐recognition technique (capsule neural networks, CapsNets) and an impact‐based autolabeling strategy. Using data from a large‐ensemble fully coupled Earth system model, CapsNets are trained on midtropospheric large‐scale circulation patterns (Z500) labeled 0–4 depending on the existence and geographical region of surface temperature extremes over North America several days ahead. The trained networks predict the occurrence/region of cold or heat waves, only using Z500, with accuracies (recalls) of 69–45% (77–48%) or 62–41% (73–47%) 1–5 days ahead. Using both surface temperature and Z500, accuracies (recalls) with CapsNets increase to ∼80% (88%). In both cases, CapsNets outperform simpler techniques such as convolutional neural networks and logistic regression, and their accuracy is least affected as the size of the training set is reduced. The results show the promises of multivariate data‐driven frameworks for accurate and fast extreme weather predictions, which can potentially augment numerical weather prediction efforts in providing early warnings. Key Points A data‐driven extreme weather prediction framework based on analog forecasting and deep learning pattern‐recognition methods is proposed Extreme surface temperature events over North America are skillfully predicted using only midtropospheric large‐scale circulation patterns More advanced deep learning methods are found to yield better forecasts, encouraging novel methods tailored for climate/weather data

Extreme physical-oceanographic events, such as marine heatwaves, fluvial floods, droughts and storm surges, have major impacts on local communities, economic sectors and ecosystems, and their frequency, intensity and duration increase due to climate change. There is a lack of understanding of the systemic drivers of extreme events as well as of their interconnected impacts on estuarine and coastal ecosystems. This knowledge is essential for assessing future impacts on ecosystem services and the local communities that depend on them, and to inform robust risk assessments and develop comprehensive risk management and adaptation strategies including early warning systems. Considering this, the German Alliance for Marine Research (DAM)-funded programme “ElbeXtreme” focuses on an integrated approach utilizing stakeholder engagement, data mining, experimental and field observations to develop novel observational and modelling approaches for assessing and monitoring risks in the Elbe estuary. The programme will deliver new insights into risks and impacts of extreme events in the estuarine system of the Elbe and the adjacent region of the North Sea (German Bight) to build a systemic risk understanding and support adaptation planning for local communities and ecosystems. Here we outline the rationale of the ElbeXtreme project and its planned activities, with the aim of stimulating national and international collaboration in tackling the urgent issue of marine and coastal risks.