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Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.

Concurrent extreme events, i.e. multi-variate extremes, can be associated with strong impacts. Hence, an understanding of how such events are changing in a warming climate is helpful to avoid some associated climate change impacts and better prepare for them. In this article, we analyse the projected occurrence of hot, dry, and wet extreme events' clusters in the multi-model ensemble of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Changes in 'extreme extremes', i.e. events with only 1% probability of occurrence in the current climate are analysed, first as univariate extremes, and then when co-occurring with other types of extremes (i.e. events clusters) within the same week, month or year. The projections are analysed for present-day climate (+1 °C) and different levels of additional global warming (+1.5 °C, +2 °C, +3 °C). The results reveal substantial risk of occurrence of extreme events' clusters of different types across the globe at higher global warming levels. Hotspot regions for hot and dry clusters are mainly found in Brazil, i.e. in the Northeast and the Amazon rain forest, the Mediterranean region, and Southern Africa. Hotspot regions for wet and hot clusters are found in tropical Africa but also in the Sahel region, Indonesia, and in mountainous regions such as the Andes and the Himalaya.

The climate is changing, and the Eastern Europe and Central Asia (ECA) region is vulnerable to the consequences. Many of the region's countries are facing warmer temperatures, a changing hydrology, and more extremes, droughts, floods, heat waves, windstorms, and forest fires. This book presents an overview of what adaptation to climate change might mean for Eastern Europe and Central Asia. It starts with a discussion of emerging best-practice adaptation planning around the world and a review of the latest climate projections. It then discusses possible actions to improve resilience organized around impacts on health, natural resources (water, biodiversity, and the coastal environment), the 'unbuilt' environment (agriculture and forestry), and the built environment (infrastructure and housing). The last chapter concludes with a discussion of two areas in great need of strengthening given the changing climate: disaster preparedness and hydro-meteorological services. This book has four key messages: a) contrary to popular perception, Eastern Europe and Central Asia face significant threats from climate change, with a number of the most serious risks already in evidence; b) vulnerability over the next 10 to 20 years is likely to be dominated by socioeconomic factors and legacy issues; c) even countries and sectors that stand to benefit from climate change are poorly positioned to do so; and d) the next decade offers a window of opportunity for ECA countries to make their development more resilient to climate change while reaping numerous co-benefits.

Extreme climatic events (ECEs) are predicted to become more frequent as the climate changes. A rapidly increasing number of studies – though few on animals – suggest that the biological consequences of ECEs can be severe. However, ecological research on the impacts of ECEs has been limited by a lack of cohesiveness and structure. ECEs are often poorly defined and have often been confusingly equated with climatic variability, making comparison between studies difficult. In addition, a focus on short‐term studies has provided us with little information on the long‐term implications of ECEs, and the descriptive and anecdotal nature of many studies has meant it is still unclear what the key research questions are. Synthesizing the current state of work is essential to identify ways to make progress. We conduct a synthesis of the literature and discuss conceptual and practical challenges faced by research on ECEs. We consider three steps to advance research. First, we discuss the importance of choosing an ECE definition and identify the pros and cons of ‘climatological’ and ‘biological’ definitions of ECEs. Secondly, we advocate research beyond short‐term descriptive studies to address questions concerning the long‐term implications of ECEs, focussing on selective pressures and phenotypically plastic responses and how they might differ from responses to a changing climatic mean. Finally, we encourage a greater focus on multi‐event studies that help us understand the implications of changing patterns of ECEs, through the combined use of modelling, experimental and observational field studies. This study aims to open a discussion on the definitions, questions and methods currently used to study ECEs, which will lead to a more cohesive approach to future ECE research.

We employ a triple difference-in-difference approach, using censuses and georeferenced temperature data, to quantify heat effects on internal migration in Central America and the Caribbean. A 1-standard deviation increase in heat would affect the lives of 7,314 and 1,578 unskilled young women and men. The effect is smaller than observed in response to droughts and hurricanes but could increase with climate change. Interestingly, youth facing heat waves are more likely to move to urban centers than when exposed to disasters endemic to the region. Research identifying the implications of these choices and interventions available to minimize distress migration is warranted.

Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed ‘climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced‐based velocity (‘dVe'); 2) threshold‐based velocity (‘ve'); and 3) gradient‐based velocity (‘gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen‐Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time‐series data. The functions in 'climetrics' are designed to be user‐friendly, making them suitable for less‐experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

The occurrence of extreme climate events in the coming years is modulated by both global warming and internal climate variability. Anticipating changes in frequency and intensity of such events in advance may help minimize the impact on climate-vulnerable sectors and society. Decadal climate predictions have been developed as a source of climate information relevant for decision-making at multi-annual timescales. We evaluate the multi-model forecast quality of the CMIP6 decadal hindcasts in predicting a set of indices measuring different characteristics of temperature and precipitation extremes for the forecast years 1-5. The multi-model ensemble skillfully predicts the temperature extremes over most land regions, while the skill is more limited for precipitation extremes. We further compare the prediction skill for these extreme indices to the skill for mean temperature and precipitation, finding that the extreme indices are predicted with lower skill, particularly those representing the most extreme days. We find only small and region-dependent improvements from model initialization in comparison to historical forcing simulations. This systematic evaluation of decadal hindcasts is essential when providing a climate service based on decadal predictions so that the user is informed on the trustworthiness of the forecasts for each specific region and extreme event.

Extreme climate events such as droughts, cold snaps, and hurricanes can be powerful agents of natural selection, producing acute selective pressures very different from the everyday pressures acting on organisms. However, it remains unknown whether these infrequent but severe disruptions are quickly erased by quotidian selective forces, or whether they have the potential to durably shape biodiversity patterns across regions and clades. Here, we show that hurricanes have enduring evolutionary impacts on the morphology of anoles, a diverse Neotropical lizard clade. We first demonstrate a transgenerational effect of extreme selection on toepad area for two populations struck by hurricanes in 2017. Given this short-term effect of hurricanes, we then asked whether populations and species that more frequently experienced hurricanes have larger toepads. Using 70 y of historical hurricane data, we demonstrate that, indeed, toepad area positively correlates with hurricane activity for both 12 island populations of Anolis sagrei and 188 Anolis species throughout the Neotropics. Extreme climate events are intensifying due to climate change and may represent overlooked drivers of biogeographic and large-scale biodiversity patterns.

In the summer of 2024, following a strong El Niño event in the preceding winter, the tropical Indian Ocean and tropical North Atlantic recorded their highest SSTs since 1961, along with a significant westward shift and intensification of the western Pacific subtropical high (WPSH). Under these conditions, China experienced its hottest summer since 1961, and was hit by a series of high-impact extreme weather and climate events. From 9 June to 2 July, southern China experienced an unprecedented extreme precipitation event that exceeded the well-known 1998 summer precipitation event in both duration and impact scope, resulting in devastating floods in the Yangtze River basin. Subsequently, in early to mid-July, the Huanghe-Huaihe Basin suffered from a severe drought–flood abrupt alternation event, heavily affecting Henan and Shandong. Meanwhile, southern China underwent a widespread heatwave event lasting 74 days, ranking as the second most intense since 1961. From late July to the end of August, northern China faced unusually frequent heavy precipitation events, with cumulative precipitation reaching the second highest for the same period since 1961, causing floods in many rivers of northern China. This study provides a timely summary and assessment of the characteristics and impacts of these extreme events. It serves as a reference for climate change research, including mechanism analysis, numerical simulation, and climate event attribution, and also offers valuable insights for improving meteorological disaster prevention and mitigation strategies.

Manmade climate change (CC) has catastrophic consequences. The United States has already experienced wholesale population realignment due to climate as households have relocated to the Sunbelt and West. The irony is that people are moving toward the heat and major storms associated with CC. As CC intensifies, with high rates of internal US factor mobility, firms and households will likely again relocate to areas with higher utility and profits, reducing CC costs. Yet current research typically focuses on CC costs in a given location without considering this realignment. We propose several avenues to overcome such shortcomings in US CC modeling.