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This chapter of the New York City Panel on Climate Change 4 (NPCC4) report provides a comprehensive description of the different types of flood hazards (pluvial, fluvial, coastal, groundwater, and compound) facing New York City and provides climatological context that can be utilized, along with climate change projections, to support flood risk management (FRM). Previous NPCC reports documented coastal flood hazards and presented trends in historical and future precipitation and sea level but did not comprehensively assess all the city's flood hazards. Previous NPCC reports also discussed the implications of floods on infrastructure and the city's residents but did not review the impacts of flooding on the city's natural and nature-based systems (NNBSs). This-the NPCC's first report focused on all drivers of flooding-describes and profiles historical examples of each type of flood and summarizes previous and ongoing research regarding exposure, vulnerability, and risk management, including with NNBS and nonstructural measures., (© 2024 The Author(s). Annals of the New York Academy of Sciences published by Wiley Periodicals LLC on behalf of The New York Academy of Sciences.)

Plant hydraulics is crucial for assessing the plants' capacity to extract and transport water from the soil up to their aerial organs. Along with their capacity to exchange water between plant compartments and regulate evaporation, hydraulic properties determine plant water relations, water status and susceptibility to pathogen attacks. Consequently, any variation in the hydraulic characteristics of plants is likely to significantly impact various mechanisms and processes related to plant growth, survival and production, as well as the risk of biotic attacks and forest fire behaviour. However, the integration of hydraulic traits into disciplines such as plant pathology, entomology, fire ecology or agriculture can be significantly improved. This review examines how plant hydraulics can provide new insights into our understanding of these processes, including modelling processes of vegetation dynamics, illuminating numerous perspectives for assessing the consequences of climate change on forest and agronomic systems, and addressing unanswered questions across multiple areas of knowledge., (© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.)

Irrigation management is essential for addressing global food security challenges under changing climate. This review discusses the integration of advanced irrigation technologies and their roles in enhancing water use efficiency and managing energy demands within agricultural systems. High-efficiency irrigation systems, such as drip and sprinkler systems, have significant potential to reduce water use and increase crop yields. However, their adoption varies worldwide, and the efficiency of existing irrigation practices often remains inadequate, resulting in substantial water losses due to outdated management practices. Emerging technologies and innovative irrigation strategies, including precision agriculture and advanced crop models, provide promising pathways for improving irrigation efficiency. Nonetheless, the widespread integration of these technologies is hindered by high costs, the need for technical expertise, and challenges in adapting existing agricultural systems to new methodologies. Irrigation systems can have substantial energy requirements, particularly those dependent on groundwater. The exploration of the water-environment-energy-food (WEEF) nexus illustrates the importance of a balanced approach to resource management, which is crucial for achieving sustainable agricultural outcomes. Future research should include lowering barriers to technology adoption, enhancing data utilization for precision irrigation, promoting integrated management strategies within the WEEF framework, and strengthening policy support for sustainable practices. This review proposes a multidisciplinary approach to irrigation management that includes technological innovation, strategic policy development, and global cooperation to secure sustainable agricultural practices and ensure global food supply resilience in the face of climate change., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Climate change is one of the most important drivers of ecosystem change, the global-scale impacts of which will intensify over the next 2 decades. Estimating the timing of unprecedented changes is not only challenging but is of great importance for the development of ecosystem conservation guidelines. Time of emergence (ToE) (point at which climate change can be differentiated from a previous climate), a widely applied concept in climatology studies, provides a robust but unexplored approach for assessing the risk of ecosystem collapse, as described by the C criterion of the International Union for Conservation of Nature's Red List of Ecosystems (RLE). We identified 3 main theoretical considerations of ToE for RLE assessment (degree of stability, multifactorial instead of one-dimensional analyses, and hallmarks of ecosystem collapse) and 4 sources of uncertainty when applying ToE methodology (intermodel spread, historical reference period, consensus among variables, and consideration of different scenarios), which aims to avoid misuse and errors while promoting a proper application of the framework by scientists and practitioners. The incorporation of ToE for the RLE assessments adds important information for conservation priority setting that allows prediction of changes within and beyond the time frames proposed by the RLE., (© 2024 Society for Conservation Biology.)

Climate change is exacerbating wildfire conditions, but evidence is lacking for global trends in extreme fire activity itself. Here we identify energetically extreme wildfire events by calculating daily clusters of summed fire radiative power using 21 years of satellite data, revealing that the frequency of extreme events (≥99.99th percentile) increased by 2.2-fold from 2003 to 2023, with the last 7 years including the 6 most extreme. Although the total area burned on Earth may be declining, our study highlights that fire behaviour is worsening in several regions-particularly the boreal and temperate conifer biomes-with substantial implications for carbon storage and human exposure to wildfire disasters., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

According to the sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC), global climate change is now unequivocal. Tunisia, like many other countries, has been affected by climate changes, including rising temperatures, intense heatwaves, and altered precipitation regimes. Tunisia's mean annual temperatures has risen about +1.4 °C in the twentieth century, with the most rapid warming taking place since the 1970s. Drought represents a primary contributing factor to tree decline and dieback. Long-term drought can result in reduced growth and health of trees, thereby increasing their susceptibility to insect pests and pathogens. Reported increases in tree mortality point toward accelerating global forest vulnerability under hotter temperatures and longer, more intense droughts. In order to assess the effect of these climate changes on the current state of forest ecosystems in Tunisia and their evolution, an investigative study was required. Here, we review the current state of knowledge on the effects of climate change on sclerophyllous and semi-deciduous forest ecosystems in Tunisia. Natural disturbance during recent years, as well as the adaptability and resilience of some forest species to climate change, were surveyed. The Standardized Precipitation Evapotranspiration Index (SPEI) is a multi-scalar drought index based on climate data that has been used to analyse drought variability. The SPEI time scale analysis showed a negative trend over the 1955-2021 period in Tunisian forest regions. In 2021, Tunisia lost 280 km 2 of tree cover to fires, which is equivalent to 26% of the total lost area between 2008 and 2021. Changing climate conditions have also affected phenological parameters, with an advance in the start of the green season (SOS) of 9.4 days, a delay at the end of the green season (EOS) of 5 days, with a consequent extended duration of the green season (LOS) by an average of 14.2 days. All of these alarming findings invite us to seek adaptation strategies for forest ecosystems. Adapting forests to climate change is therefore a challenge for scientists as well as policymakers and managers., (© 2023 John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd.)

Globally, thermodynamics explains an increase in atmospheric water vapor with warming of around 7%/°C near to the surface. In contrast, global precipitation and evaporation are constrained by the Earth's energy balance to increase at ∼2-3%/°C. However, this rate of increase is suppressed by rapid atmospheric adjustments in response to greenhouse gases and absorbing aerosols that directly alter the atmospheric energy budget. Rapid adjustments to forcings, cooling effects from scattering aerosol, and observational uncertainty can explain why observed global precipitation responses are currently difficult to detect but are expected to emerge and accelerate as warming increases and aerosol forcing diminishes. Precipitation increases with warming are expected to be smaller over land than ocean due to limitations on moisture convergence, exacerbated by feedbacks and affected by rapid adjustments. Thermodynamic increases in atmospheric moisture fluxes amplify wet and dry events, driving an intensification of precipitation extremes. The rate of intensification can deviate from a simple thermodynamic response due to in-storm and larger-scale feedback processes, while changes in large-scale dynamics and catchment characteristics further modulate the frequency of flooding in response to precipitation increases. Changes in atmospheric circulation in response to radiative forcing and evolving surface temperature patterns are capable of dominating water cycle changes in some regions. Moreover, the direct impact of human activities on the water cycle through water abstraction, irrigation, and land use change is already a significant component of regional water cycle change and is expected to further increase in importance as water demand grows with global population., (© 2020 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.)

The influences of climate change on the features of extreme rainfall events have become unprecedented that needs improved understanding at all levels for planning effective management strategies of the potential risks. This study aims to assess the potential influences of climate change on extreme rainfall characteristics in flood-vulnerable city of Adama. Daily precipitation records of 1967-2016 and projection of global circulation models (GCMs): CanESM2 and HadCM3 for 2021-2070 were disaggregated into shorter time resolutions using the Hyetos model. Gumbel type I probability distribution and power-regression model ([Formula: see text] were used for deducing intensity-duration-frequency (IDF) curves and for describing their functions, respectively. The extreme rainfall intensity of the historical and future periods for a range of storm durations and return periods were compared and contrasted. A close agreement is obtained between the observed and the modeled rainfall intensity with high values of coefficient of determination (> 0.996) and Nash-Sutcliffe efficiency (> 0.850). Besides, statistically significant (p < 0.05) direct linear relationship is found between the return periods and the coefficient parameter of the IDF models. Moreover, the intensity of extreme precipitation over 2021-2070 in Adama city would increase up to 49.5%, depending on storm duration and return period considered. This could have consequences of the way the city's drainage infrastructures are designed, operated, and sustained. Hence, flood-prone areas should be recognized in order to formulate effective strategies for mitigation and adaption of potential impacts. The standards for designing future drainage infrastructures should also be updated aiming to reflect the effects of climatic change., (© 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: Climate change has long been recognized as a significant driver of dietary diversity and dietary quality. An often overlooked aspect of climate change are shifts in fire regimes, which have the potential to drastically affect landscape diversity, species distributions, and ultimately, human diets. Here, we investigate whether the fire regimes shaped by Indigenous Australians change landscape diversity in ways that improve dietary quality, considering both the diversity and the quantity of traditional foods in the diet., Methods: We use structural equation modeling to explore two causal models of dietary quality, one focused on the direct effects of climate change and resource depression, the other incorporating the dietary effects of landscape diversity, itself a product of fire-created patchiness. We draw on a focal camp dataset covering 10 years of observations of Martu foraging income in the Western Desert of Australia., Results: We find strong support for the hypothesis that fire-created patchiness improves diet quality. Climate change (cumulative 2-year rainfall) has only an indirect effect on dietary quality; the availability of traditional foods is mediated primarily through the landscape diversity shaped by fire., Conclusions: Our model suggests that the loss of the indigenous fire mosaic may lead to worsening availability of traditional foods, measured as both caloric intake and diet diversity. Because the effects of rainfall are mediated through landscape diversity, increased rainfall may not compensate for the recent changes in fire regimes resulting from the loss of Aboriginal fire from the landscape., (© 2020 Wiley Periodicals LLC.)

ABSTRACT: Extreme precipitation events are widely thought to intensify in a warmer atmosphere through the Clausius‐Clapeyron equation. The temperature‐extreme precipitation scaling was proposed to analyse the temperature dependency of short‐duration extreme precipitation and since then, the concept has been widely used in climatology. Bao et al. (2017) suggest that the apparent scaling reflects not only how surface air properties affect extreme precipitation, but also reflects how synoptic conditions and localized cooling due to the storm itself affect the scaling – implying two‐way causality. We address here critical issues of this paper and provide evidence that dew point temperature drives extreme precipitation, with the direction of causality reversed only for the storm's peak intensity. This physical inference may serve as a basis to better quantify scaling rates and to help establish the relationship between extreme precipitation and environmental conditions in the current climate, and thereby provide insights into future changes to precipitation extremes due to climate change. [ABSTRACT FROM AUTHOR]

Very large fires (VLFs) have important implications for communities, ecosystems, air quality and fire suppression expenditures. VLFs over the contiguous US have been strongly linked with meteorological and climatological variability. Building on prior modelling of VLFs (>5000 ha), an ensemble of 17 global climate models were statistically downscaled over the US for climate experiments covering the historic and mid-21st-century periods to estimate potential changes in VLF occurrence arising from anthropogenic climate change. Increased VLF potential was projected across most historically fire-prone regions, with the largest absolute increase in the intermountain West and Northern California. Complementary to modelled increases in VLF potential were changes in the seasonality of atmospheric conditions conducive to VLFs, including an earlier onset across the southern US and more symmetric seasonal extension in the northern regions. These projections provide insights into regional and seasonal distribution of VLF potential under a changing climate, and serve as a basis for future strategic and tactical fire management options. Very large fires (VLFs) have important implications for ecosystems, air quality and fire suppression expenditures. Climate change projections suggest an increase in conditions conducive to VLF potential across most historically fire-prone regions of the US as well as changes in the seasonality of such conditions. [ABSTRACT FROM AUTHOR]

Present-day precipitation-temperature scaling relations indicate that hourly precipitation extremes may have a response to warming exceeding the Clausius-Clapeyron (CC) relation; for the Netherlands the dependency on surface dewpoint temperature follows 2 times the CC relation (2CC). The authors' hypothesis-as supported by a simple physical argument presented here-is that this 2CC behavior arises from the physics of convective clouds. To further investigate this, the large-scale atmospheric conditions accompanying summertime afternoon precipitation events are analyzed using surface observations combined with a regional reanalysis. Events are precipitation measurements clustered in time and space. The hourly peak intensities of these events again reveal a 2CC scaling with the surface dewpoint temperature. The temperature excess of moist updrafts initialized at the surface and the maximum cloud depth are clear functions of surface dewpoint, confirming the key role of surface humidity on convective activity. Almost no differences in relative humidity and the dry temperature lapse rate were found across the dewpoint temperature range, supporting the theory that 2CC scaling is mainly due to the response of convection to increases in near-surface humidity, while other atmospheric conditions remain similar. Additionally, hourly precipitation extremes are on average accompanied by substantial large-scale upward motions and therefore large-scale moisture convergence, which appears to accelerate with surface dewpoint. Consequently, most hourly extremes occur in precipitation events with considerable spatial extent. Importantly, this event size appears to increase rapidly at the highest dewpoint temperature range, suggesting potentially strong impacts of climatic warming. [ABSTRACT FROM AUTHOR]

Keywords: extreme precipitation; short-duration; flooding; climate change EN extreme precipitation short-duration flooding climate change 1 6 6 03/17/21 20210419 NES 210419 How to manage the adverse impacts of intensification of rainfall extremes remains an open question. Evaluation of CPMs show the same decrease in the hourly rainfall rates at high surface temperatures seen in observations [12] with subhourly precipitation bearing similar relationships to hourly precipitation [14]. The empirical relationship between extreme rainfall and observed temperature forms an important line of evidence for changes in short-duration rainfall extremes. CPMs are key to understanding both historical and future subdaily rainfall changes due to their ability to resolve small-scale processes which govern short-duration rainfall intensification [13]. [Extracted from the article]

Climate change induces significant changes in storm characteristics, particularly for short‐duration extreme storms (heavy rain features), impacting their intensity and spatio‐temporal distribution. Although alterations in precipitation intensity are well documented, past studies examining changes in spatio‐temporal distribution of storms (storm rainrates) were region‐specific and focused on isolated aspects of change in space or time, eluding a comprehensive understanding of the precise nature and extent of these changes. Bridging this gap, this study introduces a novel grid‐based measure of storm homogeneity, "spatio‐temporal homogeneity" metric and investigates the global patterns of change in combined spatio‐temporal characteristics of extreme storms. Analyzing the 30 min × 0.1° × 0.1° resolution Global Precipitation Measurements, the study finds that extreme storms are shrinking in both space and time due to rising surface air temperatures, predominantly in tropics. In contrast, temperate regions experience expanded extreme storms with increasing temperatures. The study also identifies a global trend toward more front‐loading in storms with rising temperatures, driven by a substantial increase in tropics and southern temperate regions. Conversely, storms in northern temperate regions become slightly more rear‐loaded as temperature increases. Furthermore, the study finds that characteristics of short–duration storms (6–12 hr) are more sensitive to temperature changes. Overall, this study contributes valuable insights into the global spatio‐temporal changes of extreme storms, highlighting regions most susceptible to alterations in storm patterns due to climate change. These findings are essential for developing effective adaptation strategies and flood management practices to cope with the changing nature of extreme storms in a warming climate. Plain Language Summary: Extreme rainfall can lead to floods, affect ecosystems, and challenge water management. How intense the rain is, how wide an area it covers, and how long it lasts are key factors in determining flood risk. Additionally, whether the rainfall starts strong and tapers off (front‐loaded) or begins gently and then fades quickly (rear‐loaded)—also plays a role. As the climate changes, it's crucial to understand how these extreme storm characteristics are evolving to better prepare for the future. This study introduces a new way to measure changes in the size and duration of extreme storms in response to rising temperatures worldwide. It also examines whether warmer temperatures make storms more likely to start strong and taper off or the opposite. The findings show that in tropical regions, extreme storms are becoming effectively shorter and covering effectively smaller areas as temperatures increase. Conversely, in temperate regions, storms are lasting longer and covering effectively larger areas. Additionally, storms in areas below 30°N are tending toward more front‐loading, while those above 30°N are becoming more rear‐loaded. This research helps us understand how warming temperatures are changing storm patterns and can inform better flood preparedness and water management strategies. Key Points: Introduces Spatio‐Temporal Homogeneity metric to effectively track comprehensive changes in storm characteristics across both space and timeRising temperature causes "spatio‐temporally peakier" storms in tropics, intense precipitation burst in smaller area over shorter durationRising temperature leads to more front‐loading in storms, notably in tropics and southern temperate regions, increasing flash flood risk [ABSTRACT FROM AUTHOR]

Wildfires burn annually over 400,000 ha in Mediterranean countries. By the end of the 21st century, wildfire Burnt Area (BA) and associated Green House Gas (GHG) emissions may double to triple due to climate change. Regional projections of future BA are urgently required to update wildfire policies. We present a robust methodology for estimating regional wildfire BA and GHG emissions under future climate change scenarios in the Mediterranean. The Fire Weather Index, selected drought indices, and meteorological variables were correlated against BA/GHG emissions data to create area-specific statistical projection models. State-of-the-art regional climate models (horizontal resolution: 12 km), developed within the EURO-CORDEX initiative, simulated data under three climate change scenarios (RCP2.6, RCP4.5, and RCP8.5) up to 2070. These data drove the statistical models to estimate future wildfire BA and GHG emissions in three pilot areas in Greece, Montenegro, and France. Wildfire BA is projected to increase by 20% to 130% up to 2070, depending on the study area and climate scenario. The future expansion of fire-prone areas into the north Mediterranean and mountain environments is particularly alarming, given the large biomass present here. Fire-smart landscape management may, however, greatly reduce the projected future wildfire BA and GHG increases. [ABSTRACT FROM AUTHOR]

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators' future projections demonstrated a tendency towards an increasing fire weather risk for the region's non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region's growing vulnerability to future fire incidents in the context of climate change. [ABSTRACT FROM AUTHOR]

The escalating threat of anthropogenic climate change has spurred an urgent quest for innovative CO2 capture and utilization (CCU) technologies. Metal–organic frameworks (MOFs) have emerged as prominent candidates in CO2 capture and conversion due to their large specific surface area, well-defined porous structure, and tunable chemical properties. This review unveils the latest advancements in MOF-based materials specifically designed for superior CO2 adsorption, precise separation, advanced photocatalytic and electrocatalytic CO2 reduction, progressive CO2 hydrogenation, and dual functionalities. We explore the strategies that enhance MOF efficiency and examine the challenges of and opportunities afforded by transitioning from laboratory research to industrial application. Looking ahead, this review offers a visionary perspective on harnessing MOFs for the sustainable capture and conversion of CO2. [ABSTRACT FROM AUTHOR]

Significant increases in temperature and precipitation due to global warming affect socioeconomics. Accurate analysis is needed for future temperature and precipitation variations across the Yangtze River basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time. The evaluation results show that the QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, while it shows improvement in reproducing precipitation. Projections indicate the annual-mean temperature will increase from 2020 to 2080 under shared socioeconomic pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest range of differences in summer. Conversely, annual-mean temperatures significantly decrease from 2081 to 2100 under SSP2-4.5. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual-mean precipitation will increase from 2020 to 2080 but decrease from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policymaking to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry). [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]

As extreme precipitation intensifies under climate change, traditional risk models based on the '100-year return period' concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California. Exposure analysis indicates approximately 53 million residents currently reside in high-risk zones, potentially almost doubling and tripling under 2 °C and 4 °C warming. Drought frequency also rises, with over 37% of major farmland vulnerable to multi-year droughts, raising agricultural risks. Record 2023 sea surface temperature anomalies suggest an impending extreme El Niño event, demonstrating the need to account for natural climate variability. The insights gained aim to inform decision-makers in shaping adaptation strategies and enhancing the resilience of communities in response to evolving extremes. [ABSTRACT FROM AUTHOR]

Nowadays, climate change is recognized as one of the biggest problems the world is facing, posing a potential threat to the environment and almost all aspects of human life. Since the United Nations Framework Convention on Climate Change in 1992, many efforts have been made to mitigate climate change, with no considerable results. According to climate change projections, temperatures will continue to rise, and extreme weather events will become more frequent, prolonged, and intense. Reflecting these concerns, the 2015 Paris Agreement was adopted as the cornerstone for reducing the impact of climate change, aiming to limit global warming below 2 °C and even keep the temperature rise below 1.5 °C. To achieve this international goal, focused mitigation actions will be required. Climate change has a strong impact on forests, enhancing their growth but also posing risks to them. Conversely, forests can mitigate climate change, as they have a considerable impact on global surface temperatures through their influence on the land–atmosphere energy exchange and the absorption of vast amounts of CO2 through photosynthesis. Consequently, afforestation and reforestation have become integral components of climate change mitigation strategies worldwide. This review aims to summarize the cutting-edge knowledge on the role of forests in climate change mitigation, emphasizing their carbon absorption and storage capacity. Overall, the impact of afforestation/reforestation on climate change mitigation hinges on strategic planning, implementation, and local forest conditions. Integrating afforestation and reforestation with other carbon removal technologies could enhance long-term effectiveness in carbon storage. Ultimately, effective climate change mitigation entails both restoring and establishing forests, alongside reducing greenhouse gas emissions. [ABSTRACT FROM AUTHOR]

In a warming climate, an intensifying fire regime and higher likelihood of extreme rain are expected to increase watershed sediment yield in many regions. Understanding regional variability in landscape response to fire and post‐fire rainfall is essential for managing water resources and infrastructure. We measured sediment yield resulting from sequential wildfire and extreme rain and flooding in the upper Carmel River watershed (116 km2), on the central California coast, USA, using changes in sediment volume mapped in a reservoir. We determined that the sediment yield after fire and post‐fire flooding was 854–1,100 t/km2/yr, a factor of 3.5–4.6 greater than the long‐term yield from this watershed and more than an order of magnitude greater than during severe drought conditions. In this first large‐scale field validation test of the WEPPcloud/wepppy framework for the Water Erosion Prediction Project (WEPP) model on a burned landscape, WEPP predicted 81%–106% of the measured sediment yield. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean climate and indicate that the increasingly widespread use of WEPP is appropriate for evaluating post‐fire hillslope erosion even across 100‐km2 scales under conditions without debris flows. Plain Language Summary: In a warming climate, more wildfire and more extreme rain will cause more erosion, producing more sediment that will be carried downstream by rivers. Understanding how much sediment a landscape produces after fire and extreme rain is essential for managing water resources and infrastructure, because sediment fills up storage space in reservoirs and can interfere with proper functioning of water systems and roads. We measured how much sediment was produced in a watershed due to wildfire followed by extreme rain and flooding, studying the Carmel River on the central California coast, USA. We evaluated how much sediment this watershed produced by measuring the volume of sediment deposited within a reservoir that the watershed drains into. We determined that the rate of sediment exported from the study watershed due to a large fire and post‐fire floods was much greater than the long‐term rate there. Having compared our results with the amount of sediment predicted by a model called the Water Erosion Prediction Project model, we found that the model performed well and realistically predicted the amount of sediment that this landscape would shed. These findings will facilitate assessing and predicting future fire effects in steep watersheds with a Mediterranean‐type climate. Key Points: A 116‐km2 central California watershed produced sediment yield of 854–1,100 t/km2/yr in the first year after a fire and extreme rainPost‐fire sediment yield in an extremely wet year without debris flows was 3.5–4.6 times greater than the long‐term rateThe Water Erosion Prediction Project model predicted 81%–106% of the measured post‐fire sediment eroded from hillslopes [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is altering the state of worldwide fire regimes, including by increasing the number of days per year when vegetation is dry enough to burn. Indices representing the percent moisture content of dead fine fuels as derived from meteorological data have been used to assess geographic patterns and temporal trends in vegetation flammability. To date, this approach has assumed a single flammability threshold, typically between 8 and 12%, controlling fire potential regardless of the vegetation type or climate domain. Here we use remotely sensed burnt area products and a common fire weather index calculated from global meteorological reanalysis data to identify and describe geographic variation in fuel moisture as a flammability threshold. This geospatial analysis identified a wide range of flammability thresholds associated with fire activity across 772 ecoregions, often well above or below the commonly used range of values. Many boreal and temperate forests, for example, can ignite and sustain wildfires with higher estimated fuel moisture than previously identified; Mediterranean forests, in contrast, tend to sustain fires with consistently low estimated fuel moisture. Statistical modelling showed that flammability thresholds derived from burnt area are primarily driven by climatological variables, particularly precipitation and temperature. Our analysis also identified complex associations between vegetation structure, fuel types, and climatic conditions highlighting the complexity in vegetation–climate–fire relationships globally. Our study provides a critical, necessary step in understanding and describing global pyrogeography and tracking changes in spatial and temporal fire activity. [ABSTRACT FROM AUTHOR]

Introduction: Climate change is predicted to increase the frequency of extreme single-day fire spread events, with major ecological and social implications. In contrast with well-documented spatio-temporal patterns of wildfire ignitions and perimeters, daily progression remains poorly understood across continental spatial scales, particularly for extreme single-day events ("blow ups"). Here, we characterize daily wildfire spread across North America, including occurrence of extreme singleday events, duration and seasonality of fire and extremes, and ecoregional climatic niches of fire in terms of Actual Evapotranspiration (AET) and Climatic Water Deficit (CWD) annual climate normals. Methods: Remotely sensed daily progression of 9,636 wildfires =400 ha was used to characterize ecoregional patterns of fire growth, extreme single-day events, duration, and seasonality. To explore occurrence, extent, and impacts of single-day extremes among ecoregions, we considered complementary ecoregional and continental extreme thresholds (Ecoregional or Continental Mean Daily Area Burned + 2SD). Ecoregional spread rates were regressed against AET and CWD to explore climatic influence on spread. Results: We found three-fold differences in mean Daily Area Burned among 10 North American ecoregions, ranging from 260 ha day-1 in the Marine West Coast Forests to 751 ha day-1 in Mediterranean California. Ecoregional extreme thresholds ranged from 3,829 ha day-1 to 16,626 ha day-1, relative to a continental threshold of 7,173 ha day-1. The ~3% of events classified as extreme cumulatively account for 16-55% of total area burned among ecoregions. We observed four-fold differences in mean fire duration, ranging from 2.7 days in the Great Plains to 10.5 days in Northwestern Forested Mountains. Regions with shorter fire durations also had greater daily area burned, suggesting a paradigm of fastgrowing short-duration fires in some regions and slow-growing long-duration fires elsewhere. CWD had a weak positive relationship with spread rate and extreme thresholds, and there was no pattern for AET. Discussion: Regions with shorter fire durations had greater daily area burned, suggesting a paradigm of fast-growing short-duration fires in some regions and slow-growing long-duration fires elsewhere. Although climatic conditions can set the stage for ignition and influence vegetation and fuels, finer-scale mechanisms likely drive variation in daily spread. Daily fire progression offers valuable insights into the regional and seasonal distributions of extreme single-day spread events, and how these events shape net fire effects. [ABSTRACT FROM AUTHOR]

Firefighters' occupational activity causes cancer, and the characterization of exposure during firefighting activities remains limited. This work characterizes, for the first time, firefighters' exposure to (coarse/fine/ultrafine) particulate matter (PM) bound polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s during prescribed fires, Fire 1 and Fire 2 (210 min). An impactor collected 14 PM fractions, the PM levels were determined by gravimetry, and the PM-bound PAHs and metal(loid)s were determined by chromatographic and spectroscopic methodologies, respectively. Firefighters were exposed to a total PM level of 1408.3 and 342.5 µg/m3 in Fire 1 and Fire 2, respectively; fine/ultrafine PM represented more than 90% of total PM. Total PM-bound PAHs (3260.2 ng/m3 in Fire 1; 412.1 ng/m3 in Fire 2) and metal(loid)s (660.8 ng/m3 versus 262.2 ng/m3), distributed between fine/ultrafine PM, contained 4.57–24.5% and 11.7–12.6% of (possible/probable) carcinogenic PAHs and metal(loid)s, respectively. Firefighters' exposure to PM, PAHs, and metal(loid)s were below available occupational limits. The estimated carcinogenic risks associated with the inhalation of PM-bound PAHs (3.78 × 10−9 − 1.74 × 10−6) and metal(loid)s (1.50 × 10−2 − 2.37 × 10−2) were, respectively, below and 150–237 times higher than the acceptable risk level defined by the USEPA during 210 min of firefighting activity and assuming a 40-year career as a firefighter. Additional studies need to (1) explore exposure to (coarse/fine/ultrafine) PM, (2) assess health risks, (3) identify intervention needs, and (4) support regulatory agencies recommending mitigation procedures to reduce the impact of fire effluents on firefighters. [ABSTRACT FROM AUTHOR]

Forests provide a wide range of ecosystem services which are important for achieving sustainable development. Anthropogenic climate change has led to the increased frequency and severity of forest fires, which imply losses of valuable ecosystem services. This paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions' area and certain socioeconomic characteristics. The climatic drivers of forest fire occurrence are explored by linking forest fires with summer and spring temperatures and precipitation, using econometric count data analysis. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Ex-post vulnerability of regions to forest fires based on physical and socioeconomic characteristics is calculated, and expected changes in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios are predicted. Relative vulnerability estimates and the impact of specific climatic drivers on forest fires will be useful in designing policies for preserving forests as natural capital and promoting sustainability. [ABSTRACT FROM AUTHOR]

Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO 2 ; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO 2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO 2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate. [ABSTRACT FROM AUTHOR]

Global storm‐resolving models (GSRMs) that can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional Coupled Model Intercomparison Project 6 (CMIP6) models. In this study, we present results from two‐year‐long integrations of a GSRM developed at Geophysical Fluid Dynamics Laboratory, eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), for the response of precipitation to sea surface temperature warming and an isolated increase in CO2 and compare it to CMIP6 models. At leading order, X‐SHiELD's response is within the range of the CMIP6 models. However, a close examination of the precipitation distribution response reveals that X‐SHiELD has a different response at lower percentiles and the response of the extreme events are at the lower end of the range of CMIP6 models. A regional decomposition reveals that the difference is most pronounced for midlatitude land, where X‐SHiELD shows a lower increase at intermediate percentiles and drying at lower percentiles. Plain Language Summary: The main tool to investigate and make projections of the response of the climate system to warming is global climate models. These models usually have ∼100 km horizontal resolution and as a result need parameterizations in order to account for small‐scale processes. These parameterizations are a large source of uncertainty. Advancements in computational capabilities and technology allow us to run global models with high enough resolutions, called global storm‐resolving models (GSRMs), that some important small‐scale processes are explicitly resolved. In this study, we use two‐year‐long integrations of an atmosphere GSRM in present‐day and warmer climates and compare the precipitation response to more traditional global climate models. We find that the precipitation response of the GSRM lies within the range of the traditional models, except for the low percentiles of precipitation. A closer examination indicates that the main source of differences between the GSRM and traditional models occurs in midlatitude land regions, where the GSRM predicts more extensive drying of low precipitation percentiles compared to the traditional models. The GSRM also has an increase in precipitation extremes with warming that is somewhat weaker than that of traditional global climate models. Key Points: We compare precipitation changes between Coupled Model Intercomparison Project 6 (CMIP6) models and eXperimental System for High‐resolution prediction on Earth‐to‐Local Domains (X‐SHiELD), a global storm resolving model developed at Geophysical Fluid Dynamics LaboratoryX‐SHiELD response is mostly within the range of CMIP6 models, except for more drying of low precipitation percentilesX‐SHiELD precipitation distribution changes over midlatitude land differs significantly from the CMIP6 models [ABSTRACT FROM AUTHOR]

The Mediterranean Basin is one of the regions most affected by climate change, which poses significant challenges to agricultural efficiency and food security. While rising temperatures and decreasing precipitation levels already impose great risks, the effects of compound extreme events (CEEs) can be significantly more severe and amplify the risk. It is therefore of high importance to assess these risks under climate change on a regional level to implement efficient adaption strategies. This study focuses on false-spring events (FSEs), which impose a high risk of crop losses during the beginning of the vegetation growing period, as well as heat–drought compound events (HDCEs) in summer, for a high-impact future scenario (Representative Concentration Pathway (RCP) 8.5). The results for 2070–2099 are compared to 1970–1999. In addition, deviations of the near-surface atmospheric state under FSEs and HDCEs are investigated to improve the predictability of these events. We apply a multivariate, trend-conserving bias correction method (MBCn) accounting for temporal coherency between the inspected variables derived from the European branch of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX). This method proves to be a suitable choice for the assessment of percentile-threshold-based CEEs. The results show a potential increase in frequency of FSEs for large portions of the study domain, especially impacting later stages of the warming period, caused by disproportionate changes in the behavior of warm phases and frost events. Frost events causing FSEs predominantly occur under high-pressure conditions and northerly to easterly wind flow. HDCEs are projected to significantly increase in frequency, intensity, and duration, mostly driven by dry, continental air masses. This intensification is several times higher than that of the univariate components. This study improves our understanding of the unfolding of climate change in the Mediterranean and shows the need for further, locally refined investigations and adaptation strategies. [ABSTRACT FROM AUTHOR]

Wildfires are a natural part of the dynamics of Mediterranean forest ecosystems. The fire patterns in the Mediterranean basin have been altered mainly due to changes in land use and climate change. In 2017, a wildfire in Yeste (Spain) burned 3200 hectares of two Mediterranean pine forests. We investigated the effects of burn severity and postfire salvage logging practices on vegetation and soil properties in four experimental areas distributed within the wildfire perimeter. These areas included unburned, low, high, and high burn severity with salvage logging, all located under Pinus halepensis Mill and Pinus pinaster Aiton stands. Salvage logging was applied 18 months after the fire. We established 72 circular plots (nine per treatment and pine species). We collected soil samples to analyze physicochemical and biological soil properties, including pH, electrical conductivity (EC), soil organic matter (SOM) content, carbon from microbial biomass (CBM), basal soil respiration (BSR), metabolic quotient (qCO2), and two enzymatic activities: β-glucosidase (GLU) and phosphatase (PHP). To understand how vegetation changed after fire, we implemented three linear transects per plot to calculate α-diversity indices (richness, Shannon, and Simpson), vegetation coverage (COBV), fraction of bare soil (BSOIL), the number of postfire seedlings (NSeed) and their average height (Hm), and we grouped vegetation into different postfire adaptive strategies: facultative seeder (R+S+), obligate resprouter (R+S−), obligate seeder (R−S+), and non-fire-adapted (R−S−). We ran ANOVA and Tukey's HSD post hoc tests to evaluate the differences between burn severity and salvage logging practices on the variables examined for each pine stand. We used PCA and correlation analysis to identify plant-soil interactions. Our results suggest that Pinus halepensis stands were more affected by the wildfire than Pinus pinaster stands due to the distinct characteristics of each species (morphology of the leaves, bark thickness, cone structure, etc.) and the significant differences observed in terms of pH, SOM, CBM, qCO2, GLU, PHP, and Nseed. The proportion of obligate resprouter species was higher in Pinus halepensis stands, and the obligate seeder species were higher in Pinus pinaster stands. The study highlighted the importance of monitoring burn severity and postfire management practices to promote forest recovery and reduce wildfire risk. Limiting the negative impact of postfire salvage logging practices can enhance the resilience of vulnerable ecosystems. [ABSTRACT FROM AUTHOR]

Taking advantage of a large ensemble of Convection Permitting‐Regional Climate Models on a pan‐Alpine domain and of an object‐oriented dedicated analysis, this study aims to investigate future changes in high‐impact fall Mediterranean Heavy Precipitation Events at high warming levels. We identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial extension of the affected areas, for a dominant influence of the driving Global Climate Models for projecting changes in the frequency, and for an increase in intensity, area, volume and severity over the French Mediterranean. However, large quantitative uncertainties persist despite the use of convection‐permitting models, with no clear agreement in frequency changes over southeastern France and a large range of plausible changes in events' properties, including for the most intense events. Model diversity and international coordination are still needed to provide policy‐relevant climate information regarding precipitation extremes. Plain Language Summary: Despite growing computational resources and multiple model developments, projecting future changes in the high‐impact Mediterranean Heavy Precipitation Events remains both a numerical and scientific challenge. The present study takes advantage of the recent availability of a relatively large ensemble of high resolution Regional Climate Models (2–3 km), which represent a step change in the simulation of precipitation extremes, and of an object‐oriented approach, allowing us to track the convective precipitating systems on an hourly basis. Looking at future changes in fall Mediterranean Heavy Precipitation Events at high warming levels, we identify a robust multi‐model agreement for an increased frequency from central Italy to the northern Balkans combined with a substantial expansion of the affected areas, and an increase in intensity, area, volume and severity over the French Mediterranean. However, considerable uncertainties remain in terms of frequency over parts of the domain arising from uncertainty in changes in large scale weather patterns, and in terms of degree of intensification for the most intense events. It suggests the need for model diversity and for more coordinated high resolution climate projections with careful selection of different driving global models in order to provide policy‐relevant climate information regarding precipitation extremes. Key Points: High‐resolution ensemble and object‐oriented approach offer a unique opportunity to study changes in Mediterranean extreme precipitationRobust agreement is found for an increase in intensity, volume and severity for future French Mediterranean Heavy Precipitation EventsEven at convection‐permitting scale, considerable uncertainty remains regarding the degree of intensification of the most extreme events [ABSTRACT FROM AUTHOR]

Fire suppression is the primary management response to wildfires in many areas globally. By removing less-extreme wildfires, this approach ensures that remaining wildfires burn under more extreme conditions. Here, we term this the "suppression bias" and use a simulation model to highlight how this bias fundamentally impacts wildfire activity, independent of fuel accumulation and climate change. We illustrate how attempting to suppress all wildfires necessarily means that fires will burn with more severe and less diverse ecological impacts, with burned area increasing at faster rates than expected from fuel accumulation or climate change. Over a human lifespan, the modeled impacts of the suppression bias exceed those from fuel accumulation or climate change alone, suggesting that suppression may exert a significant and underappreciated influence on patterns of fire globally. Managing wildfires to safely burn under low and moderate conditions is thus a critical tool to address the growing wildfire crisis. Fire suppression removes less-extreme wildfires, concentrating fires under extreme conditions. The authors use model simulations to show how this "suppression bias" intensifies fire behavior and effects, beyond fuel accumulation and climate change impacts. [ABSTRACT FROM AUTHOR]

Flood hazards have led to substantial fatalities and economic loss in the last five decades, making it essential to understand flood dynamics in a warming climate. This study reports the first comprehensive assessment of projected flood hazards across Vietnam. We used downscaled climate data from the CMIP6 initiative, involving a total of 20 climate models, and streamflow projection simulated using a semi-distributed hydrological model. The assessment covers seven near-natural catchments, each representing a climate zone of the country. To evaluate climate change impacts on floods, the study simultaneously analyzes changes in three indices: (i) the annual hottest day temperature, to represent temperature extremes; (ii) the maximum daily rainfall amount, to represent rainfall extremes; and (iii) the discharge value exceeding 5% in a year, to assess streamflow extremes. Changes in the selected indices (relative to the reference period from 1985 to 2014) are assessed under four emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) and two future time slices (2036–2065 and 2070–2099). Although the robustness (as indicated by multi-model agreement) and significance (identified through the statistical test) of the changes vary substantially, depending on the selected indices and assessed time slices, an overall increase is consistently identified across all of the assessed hydro-climatic extremes (up to 4.8 °C for temperature extremes, 43 mm for rainfall extremes, and 31% for streamflow extremes). The findings suggest a potential increase in flood risk across Vietnam in a warming climate, highlighting the urgent need for improved flood preparedness and investment to reduce economic loss and mortality in an uncertain future. [ABSTRACT FROM AUTHOR]

Concurrent meteorological extremes (CMEs) represent a class of pernicious climatic events characterized by the coexistence of two extreme weather phenomena. Specifically, the juxtaposition of Urban Extreme Rainfall (UER) and Urban Extreme Heat (UEH) can precipitate disproportionately deleterious impacts on both ecological systems and human well-being. In this investigation, we embarked on a meticulous risk appraisal of CMEs within China's Greater Bay Area (GBA), harnessing the predictive capabilities of three shared socioeconomic pathways (SSPs) namely, SSP1-2.6, SSP3-7.0, and SSP5-8.5, in conjunction with the EC-Earth3-Veg-LR model from the CMIP6 suite. The findings evidence a pronounced augmentation in CME occurrences, most notably under the SSP1-2.6 trajectory. Intriguingly, the SSP5-8.5 pathway, typified by elevated levels of greenhouse gas effluents, prognosticated the most intense CMEs, albeit with a temperate surge upon occurrence. Additionally, an ascendant trend in the ratio of CMEs to the aggregate of UER and UEH portends an escalating susceptibility to these combined events in ensuing decades. A sensitivity analysis accentuated the pivotal interplay between UER and UEH as a catalyst for the proliferation of CMEs, modulated by alterations in their respective marginal distributions. Such revelations accentuate the imperative of assimilating intricate interdependencies among climatic anomalies into evaluative paradigms for devising efficacious climate change countermeasures. The risk assessment paradigm proffered herein furnishes a formidable instrument for gauging the calamitous potential of CMEs in a dynamically shifting climate, thereby refining the precision of prospective risk estimations. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate. [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

This paper presents the methodology for the construction of the KNMI'23 national climate scenarios for the Netherlands. We have developed six scenarios, that cover a substantial part of the uncertainty in CMIP6 projections of future climate change in the region. Different sources of uncertainty are disentangled as much as possible, partly by means of a storyline approach. Uncertainty in future emissions is covered by making scenarios conditional on different SSP scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). For each SSP scenario and time horizon (2050, 2100, 2150), we determine a global warming level based on the median of the constrained estimates of climate sensitivity from IPCC AR6. The remaining climate model uncertainty of the regional climate response at these warming levels is covered by two storylines, which are designed with a focus on the annual and seasonal mean precipitation response (a dry‐trending and wet‐trending variant for each SSP). This choice was motivated by the importance of future water management to society. For users with specific interests we provide means how to account for the impact of the uncertainty in climate sensitivity. Since CMIP6 GCM data do not provide the required spatial detail for impact modeling, we reconstruct the CMIP6 responses by resampling internal variability in a GCM‐RCM initial‐condition ensemble. The resulting climate scenarios form a detailed storyline of plausible future climates in the Netherlands. The data can be used for impact calculations and assessments by stakeholders, and will be used to inform policy making in different sectors of Dutch society. Plain Language Summary: To prepare society for the effects of future climate change, we need to know what the future climate will be like. In this paper we explain the method that is used to construct six different scenarios that describe possible future climates of the Netherlands. The scenarios make assumptions about future greenhouse gas emissions, and are based on the outcomes of climate models that simulate the response of the climate to these emissions. The KNMI'23 climate scenarios show that strongly reducing global emissions strongly reduces the expected changes in the climate of the Netherlands. In the scenario in which global emissions continue to rise until 2080, Dutch society will have to adapt to a much stronger increases in heat and precipitation extremes, increased risks of droughts with low river discharge in summer, and increased risk of flooding due to high river discharges in winter. In the coming years the climate scenario data will be used to evaluate what needs to be done to keep the country a safe place for people to live in and to thrive in, under changing climate conditions. Key Points: We present a methodology for the construction of regional climate scenarios using a storyline approach to partition uncertaintyResults from CMIP6 are reconstructed with a GCM‐RCM initial condition ensemble to produce high‐resolution scenario data for end‐usersSix scenario variants cover emission uncertainty (high, moderate, low) and uncertainty in the regional response (dry‐trending, wet‐trending) [ABSTRACT FROM AUTHOR]

Climate change poses a significant threat to agriculture and livestock production in sub-Saharan Africa, a region heavily reliant on livestock for smallholder farmers' livelihoods. This systematic review investigates the potential of communally established cattle feedlots as a sustainable strategy to address the interconnected challenges of climate change resilience and food security in the area. The review focuses on the intensification of climate change, marked by rising temperatures and altered precipitation patterns, posing a direct threat to the livelihoods of millions in the region. Conducting a systematic literature review, we meticulously analyzed 72 articles that centered on communally established cattle feedlots in sub-Saharan Africa. The inclusion criteria considered studies within the context of climate change resilience and food security, utilizing both qualitative and quantitative methodologies. Published articles, grey literature, and relevant reports were systematically sourced from academic databases such as PubMed, Scopus, Google Scholar and Web of Science, complemented by manual searches of journals, conference proceedings, and organizational websites. The synthesis of findings reveals a nuanced landscape of successes and challenges associated with communal feedlots. Through a narrative synthesis, studies were categorized based on key themes, unraveling the impact of communal feedlots on livestock health, economic viability, and socio-economic dynamics. The review highlights the role of communal feedlots in mitigating climate-related shocks, enhancing livestock productivity, and fostering economic opportunities for smallholder farmers. However, challenges related to land tenure, community engagement, and resource allocation emerged as critical considerations. In conclusion, communally established cattle feedlots offer a holistic and sustainable approach to address climate change challenges in sub-Saharan Africa. [ABSTRACT FROM AUTHOR]

A string of fierce fires broke out in Chile in the austral summer 2023, just six years after the record-breaking 2017 fire season. Favored by extreme weather conditions, fire activity has dramatically risen in recent years in this Andean country. A total of 1.7 million ha. burned during the last decade, tripling figures of the prior decade. Six of the seven most destructive fire seasons on record occurred since 2014. Here, we analyze the progression during the last two decades of the weather conditions associated with increased fire risk in Central Chile (30°–39° S). Fire weather conditions (including high temperatures, low humidity, dryness, and strong winds) increase the potential for wildfires, once ignited, to rapidly spread. We show that the concurrence of El Niño and climate-fueled droughts and heatwaves boost the local fire risk and have decisively contributed to the intense fire activity recently seen in Central Chile. Our results also suggest that the tropical eastern Pacific Ocean variability modulates the seasonal fire weather in the country, driving in turn the interannual fire activity. The signature of the warm anomalies in the Niño 1 + 2 region (0°–10° S, 90° W–80° W) is apparent on the burned area records seen in Central Chile in 2017 and 2023. [ABSTRACT FROM AUTHOR]

The Yangtze River Economic Belt (YREB) is an important strategic area in China. However, frequent extreme precipitation events have caused great economic losses and human casualties in this region. In this article, we explore the spatial and temporal links between extreme precipitation events and Sunspot Number (SSN), El Niño Southern Oscillation (ENSO), Arctic Oscillation (AO) and Pacific Decadal Oscillation (PDO) in this important economic belt. According to the research findings, all of the extreme precipitation indices (EPIs) except for consecutive dry days (CDD) and consecutive wet days (CWD) showed an upward trend in the YREB over the 59 years. The spatial distributions of very wet days (R95p), extremely wet days (R99p), max 1-day precipitation amount (Rx1day) and max 5-day precipitation amount (Rx5day) had similar distribution patterns, showing decreasing trends from east to west. The EPIs generally had a 2–4-year band, suggesting stronger and more elusive changes. The wavelet coherence (WTC) spectra suggested that SSN, ENSO, AO, and PDO have different effects on extreme precipitation events during different time periods. Before 1985, the SSN, ENSO, AO, PDO and extreme precipitation events shared similar oscillation periods, but after 1985, their oscillation periods were no longer consistent with each other. In addition, solar activity and the AO mainly had negative correlations with extreme precipitation events, while the ENSO and PDO had predominantly positive correlations with the EPIs. This paper provides a reference for national economic strategic planning and natural resource management in the YREB. [ABSTRACT FROM AUTHOR]

The present study analyses the climate change response of Alpine-Mediterranean heavy precipitation events (HPEs), often associated with hazardous flooding. We investigate HPE properties describing their propagation, scale and intensity, by applying a storm tracking algorithm in an ensemble of convection permitting regional climate models (cpRCMs) under the CMIP5-RCP85 forcing scenario. The climate change response of HPE properties we derive by comparing their mean values for periods nearfuture [2040, 2050] and farfuture [2090, 2100] against those of a present-day period historical [1996, 2005]. By the end of the 21st century the model ensemble projects the region's surface temperatures to increase by 4.0 ∘ C. In this warmer climate HPEs are found to propagate farther and faster. They last longer by 5%, their area increases by 15% and their precipitation volume by 35%. Their maximum precipitation rate increases by 13% and an estimate of severity is found increased by 20%. By differentiating specific HPE categories, regions and seasons we find that landfalling and orographically forced HPEs occurring in fall are most intense, heavy and severe. Most importantly we find that these events are also projected to respond most strongly to climate warming, meaning that they will be even more intense, heavy and severe. Consequently in Mediterranean regions the most dramatic changes must be expected, in particular in Southern Italy. But also in regions north of the Alps, where HPEs are generally weaker than to the south, HPEs are found to occur twice as often in wintertime accompanied with an increase in precipitation volume of about 50%. Our analysis also confirms that even though the Mediterranean climate is generally drier by the year 2100, precipitation amounts associated with HPEs are found to increase by 52%. Further HPEs are projected to be 12% more frequent overall, although their occurrence frequency decreases in summer. Eventually we show that the characteristic HPE properties scale with surface warming, meaning that the warmer the local climate is, the more frequent, intense, heavy and severe are HPEs. Notably statistical tests show that the ten-member cpRCM ensemble provides robust climate change responses of all HPE properties investigated. The projected changes of HPE properties infer an increase of flooding risk for communities of the Alpine-Mediterranean area and thus the results presented here may encourage local authorities in adapting their flood protection measurements to climate change. [ABSTRACT FROM AUTHOR]

Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement: The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment. [ABSTRACT FROM AUTHOR]

Large wildfires (>125 hectares) in the United States account for over 95% of the burned area each year. Predicting large wildfires is imperative; however, current wildfire predictive models are region-based and computationally intensive. Using a scalable model based on easily available environmental and atmospheric data, this research aims to accurately predict whether large wildfires will develop across the United States. The data used in this study include 2109 wildfires over 20 years, representing 14 million hectares burned. Remote sensing environmental data (Normalized Difference Vegetation Index—NDVI; Enhanced Vegetation Index—EVI; Leaf Area Index—LAI; Fraction of Photosynthetically Active Radiation—FPAR; Land Surface Temperature during the Day—LST Day; and Land Surface Temperature during the Night—LST Night) consisting of 1.3 billion satellite observations was used. Atmospheric reanalysis data (u component of wind, v component of wind, relative humidity, temperature, and geopotential) at four pressure levels (300, 500, 700, and 850 Ha) were also factored in. Six machine learning classification models (Logistic Regression, Decision Tree, Random Forest, eXtreme Gradient Boosting, K-Nearest Neighbors, and Support Vector Machine) were created and tested on the resulting dataset to determine their accuracy in predicting large wildfires. Model validation tests and variable importance analysis were performed. The eXtreme Gradient Boosting (XGBoost) classification model performed best in predicting large wildfires, with 90.44% accuracy, a true positive rate of 0.92, and a true negative rate of 0.88. Furthermore, towards environmental justice, an analysis was performed to identify disadvantaged communities that are also vulnerable to wildfires. This model can be used by wildfire safety organizations to predict large wildfires with high accuracy and prioritize resource allocation to employ protective safeguards for impacted socioeconomically disadvantaged communities. [ABSTRACT FROM AUTHOR]

Microclimate heterogeneity in Southeast Asia and its implications for biodiversity remain understudied. We investigated microclimate changes between 1982 and 2017 using high‐resolution data and a patch‐mosaic model. Our findings reveal a decline in microclimate heterogeneity (as indicated by Shannon's diversity index) and a significant increase in elevation. Topographical factors (∼50.6%) and climatic factors (∼21.5%) primarily influenced the decline. Spatial heterogeneity weakened with larger moving windows and coarser grid‐scale data sets. Our study suggests that shifting mountainous microclimates play a crucial role in mediating biodiversity in Southeast Asia. Plain Language Summary: In response to climate change, research on microclimates with potential ecological significance has become increasingly popular. Despite Southeast Asia being an exceptionally rich area for biodiversity, only a limited number of studies have been conducted to explore the intricate heterogeneity of fine‐scale microclimates in this region. Furthermore, the impact of potential environmental driving forces on this heterogeneity remains scarcely explored. Within this study, we used high‐resolution data sets and adopted a "patch‐mosaic" model paradigm to examine changes in microclimate heterogeneity, quantified by Shannon's diversity index (SHDI), across Southeast Asia from 1982 to 2017. Our findings unveil a discernible reduction in SHDI between the periods 1982–1999 and 2000–2017, concomitant with a marked elevation increase. Deconstructing the attribution analysis within 39 mountainous areas revealed that topographical factors (∼50.6%) and climatic factors (∼21.5%) were the foremost influences underpinning declines in SHDI within these mountainous areas. We also found that the spatial heterogeneity of the microclimates weakened as the moving window size increased and the grid‐scale data set became coarser. We suggest that biodiversity in Southeast Asia is likely mediated by shifting mountainous microclimates. Key Points: A high‐resolution climate data set was used to delineate the fine‐scale microclimate variability in Southeast AsiaTopographic roughness plays an important role in mediating the evolution of microclimate heterogeneitySampling size and resolution clearly affect the identification of microclimate heterogeneity [ABSTRACT FROM AUTHOR]

Extreme weather events, such as tropical cyclones, often trigger population displacement. The frequency and intensity of tropical cyclones are affected by anthropogenic climate change. However, the effect of historical climate change on displacement risk has so far not been quantified. Here, we show how displacement can be partially attributed to climate change using the example of the 2019 Tropical Cyclone Idai in Mozambique. We estimate the population exposed to high water levels following Idai's landfall using a combination of a 2D hydrodynamical storm surge model and a flood depth estimation algorithm to determine inland flood depths from remote sensing images, factual (climate change) and counterfactual (no climate change) mean sea level, and maximum wind speed conditions. Our main estimates indicate that climate change has increased displacement risk from this event by approximately 12 600–14 900 additional displaced persons, corresponding to about 2.7 % to 3.2 % of the observed displacements. The isolated effect of wind speed intensification is double that of sea level rise. These results are subject to important uncertainties related to both data and modeling assumptions, and we perform multiple sensitivity experiments to assess the range of uncertainty where possible. Besides highlighting the significant effects on humanitarian conditions already imparted by climate change, our study provides a blueprint for event-based displacement attribution. [ABSTRACT FROM AUTHOR]

Within the past decade, the attribution of extreme weather events and their impacts has enabled scientists, the public, and policymakers alike to connect real-world experiences of extreme weather events with scientific understanding of anthropogenic climate change. Attribution studies of recent extreme weather events have formed a new and important line of evidence in the most recent Intergovernmental Panel on Climate Change (IPCC) assessment report understanding present-day impacts of climate change. IPCC studies using different methods of event attribution have been assessed together, highlighting that these differences are smaller than the academic discourse on the methods suggests. This development raised two important research questions the science needs to answer: First, how do we formally combine attribution statements using highly conditional methods with probabilistic assessments of how climate change alters the likelihood and intensity of extreme weather events? Second, under what circumstances are individual attribution studies still necessary and to what extent do existing attribution studies provide enough information to answer societal questions? Furthermore, the scientific development still leaves important gaps, particularly in countries of the Global South, leading to ethical questions around the need and requirement of attribution of extreme events in policy contexts, informing adaptation and loss and damage and the role of vulnerability. [ABSTRACT FROM AUTHOR]