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Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC). The study utilized historical climate data along with projections from the CMIP6 IPSL-CM6A-LR model for the period 2021–2100. Several indices, such as evapotranspiration, were calculated, and factors like soil composition and topography were integrated into the classification of regions based on their agricultural potential. Critical variables in this assessment included temperature, precipitation, and water or thermal excess. The results showed that 6.59% of South America's territory is currently suitable for barley cultivation without additional irrigation, with these regions concentrated primarily in temperate southern areas. In contrast, 18.62% of the region is already unsuitable due to excessive heat. Projections under future climate scenarios indicate a shrinking of suitable areas, alongside an expansion of unsuitable regions. In the worst-case scenario, only 1.48% of the territory would remain viable for barley farming. These findings emphasize the crop's vulnerability to climate change, underscoring the urgency of developing agricultural adaptation strategies. The predicted contraction in suitable barley cultivation areas demonstrates the profound impact of climate change on agriculture and highlights the need for proactive measures to ensure sustainable barley production in South America. [ABSTRACT FROM AUTHOR]

ABSTRACT With the intensification of climate change, there is an increasing need to assess its potential impacts on hydrology and water resource systems. The reference evapotranspiration (ETo) plays a crucial role as an indicator for calculating the hydrological cycle and understanding these effects. The main objective of this study was to analyse the projected changes in simulated ETo over Egypt until the end of the 21st century. This analysis was conducted using the global climate models (GCMs) of the latest phase of the Coupled Model Intercomparison Project (CMIP6) framework, which incorporates the shared socioeconomic pathways (SSPs)—SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5. The Penman–Monteith equation was applied to calculate ETo utilising data from four CMIP6 GCMs for the historical (1970–2014) and two future periods, the near future (2020–2059) and far future (2060–2100). The results revealed an overall increase in ETo for all scenarios and periods. The highest increase in annual ETo was observed under SSP5‐8.5, reaching 14.2% during the far future, while the lowest projected increase was 4.36% for SSP1‐2.6 in the near future. In addition, the projected ETo demonstrated the greatest increase during winter, while the lowest increase was in summer. Geographically, the increases will be more in the southwest and the least in the southeast for all scenarios and future periods. These findings emphasise the potential consequences that Egypt, a global water stress hotspot, could face if ETo rises due to escalating temperatures. It underscores the importance of addressing these challenges to ensure the sustainability of water resources in the face of climate change. [ABSTRACT FROM AUTHOR]

While deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM) over New Zealand. Both approaches emulate future changes in annual mean precipitation well, when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/° $\mathit{{}^{\circ}}$C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming‐driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data. Plain Language Summary: The resolution of climate models (∼150 km) is too coarse for studying the effects of climate change at regional scales. The resolution can be enhanced or "downscaled" by a physics‐based method known as dynamical downscaling, but it is costly and limits the number of climate models that can be downscaled. Deep learning approaches offer a promising and computationally efficient alternative to dynamical downscaling, but it is unclear whether their downscaling of climate models produces plausible and reliable climate projections. We show that one commonly used deep‐learning algorithm underestimates future projections of extreme rainfall. However, we show that another algorithm known as a Generative Adversarial Network is better suited for predicting future changes in extreme rainfall and could be useful in similar applications. Key Points: A deterministic (regression) downscaling method underestimates future increases in extreme precipitation, even when trained on future climatesGenerative Adversarial Networks (GANs) tested here better capture these future increases than deterministic methods, even when trained historicallyGANs trained on future climates have better historical and future extrapolation skill versus historical training [ABSTRACT FROM AUTHOR]

Introduction: This paper examines the role of agricultural advisors as key partners for scaling adoption of long-term climate information. Agri-food sectors across the world face significant challenges in responding to climate change, which intersect with broader pressures driving transitions to more climate resilient and sustainable agri-food systems. Making better climate information available to farmers is a key part of responding to these challenges, since relevant and usable climate information can help farmers to adapt to future climate conditions. The development of climate services, which seek to provide climate information to assist with decision making, has therefore increased significantly over the last decade. The Climate Services for Agriculture (CSA) program provides long-term climate projections to help the Australian agriculture sector prepare for and adapt to future climate conditions. 'My Climate View' is an online tool produced by CSA, which provides localised and contextualised, commodity-specific climate information, through historic weather data and multi-decadal projections of future climate, aimed at Australian famers and farm advisors. Agricultural advisors have a critical yet often underutilised role as climate information intermediaries, through assisting farmers translate climate information into action. Methods: This paper uses CSA as a case study to examine farmer-advisor interactions as a key adoption pathway for My Climate View. We interviewed 52 farmers and 24 advisors across Australia to examine the role of advisors as key partners in helping farmers to understand climate information and explore on-farm climate adaptation options. Results and discussion: Interactions between farmers and their trusted advisors are an essential part of the enabling environment required to ensure that this long-term climate information can be used at the farm scale to inform longer-term decisions about climate adaptation. We use the concept of an interaction space to investigate farmer-advisor interactions in the adoption and sustained use of My Climate View. We find that although My Climate View is not a transformational technology on its own, its ability to enable farmers and advisors to explore and discuss future climate conditions and consider climate adaptation options has the potential to support transformational changes on-farm that are needed to meet the sustainability transition pressures that climate change presents. [ABSTRACT FROM AUTHOR]

There is an urgent need for models that can robustly detect past and project future ecosystem changes and risks to the services that they provide to people. The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) was established to develop model ensembles for projecting long‐term impacts of climate change on fisheries and marine ecosystems while informing policy at spatio‐temporal scales relevant to the Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP) framework. While contributing FishMIP models have improved over time, large uncertainties in projections remain, particularly in coastal and shelf seas where most of the world's fisheries occur. Furthermore, previous FishMIP climate impact projections have been limited by a lack of global standardized historical fishing data, low resolution of coastal processes, and uneven capabilities across the FishMIP community to dynamically model fisheries. These features are needed to evaluate how reliably the FishMIP ensemble captures past ecosystem states ‐ a crucial step for building confidence in future projections. To address these issues, we have developed FishMIP 2.0 comprising a two‐track framework for: (a) Model evaluation and attribution of past changes and (b) future climate and socioeconomic scenario projections. Key advances include improved historical climate forcing, which captures oceanographic features not previously resolved, and standardized global fishing forcing to test fishing effects systematically across models. FishMIP 2.0 is a crucial step toward a detection and attribution framework for changing marine ecosystems and toward enhanced policy relevance through increased confidence in future ensemble projections. Our results will help elucidate pathways toward achieving sustainable development goals. Plain Language Summary: Historically, the largest human impact on the ocean has been overfishing. In the future, it may become climate change. To understand and predict how human activities will affect marine ecosystems in the future, we need models that can be used to accurately detect and attribute the effects of drivers and their impact on past ecosystem trajectories. By doing this, we will build confidence in the ability of sets of these models ("ensembles") to capture future change. FishMIP 2.0 provides a way to construct and test these ensembles and scenarios of both changing climate and socio‐economic conditions, to better assess how future fisheries could adapt over time. Key Points: Detecting, attributing, and projecting climate change risks on marine ecosystems and fisheries requires models with realistic dynamicsFishMIP 2.0 incorporates fishing and climate impact trajectories to assess models and detect past ecosystem changes more accuratelyOur framework will help support model improvement, building confidence in future projections to underpin policy advice [ABSTRACT FROM AUTHOR]

Background: The Middle East, especially Kuwait, is experiencing rapidly rising temperatures due to climate change. Cardiovascular diseases (CVD) are the leading cause of mortality in the country, and extreme heat is expected to exacerbate hospitalizations for cardiovascular diseases. There is limited data quantifying the historical and future impacts of heat on hospitalizations for cardiovascular diseases in Kuwait. Methods: We collected daily hospital admission data of cardiovascular diseases in Kuwait from 2010 to 2019. We modeled the relationship between temperature and cardiovascular disease hospitalizations using distributed lag non-linear models (DLNMs), adjusting for relative humidity and seasonality. Future temperature projections for Kuwait under moderate and extreme climate change scenarios were obtained from the Coupled Model Inter-comparison Project Phase 6 (CMIP6), and the impact on cardiovascular disease hospitalizations was extrapolated for every decade until 2099. Results: During the baseline period (2010–2019), a total of 263,182 CVD cases were recorded. Of which, 20,569 (95% eCI: 3,128, 35,757) were attributed to heat. We found that the relative risk of hospitalization for CVD increased from 1.292 (95% CI: 1.051, 1.589) at 41 °C to 1.326 (95% CI: 1.006, 1.747) at 43 °C, compared to the minimum morbidity temperature. Projections showed that, under moderate climate scenarios, CVD hospitalizations would increase by 1.96% by 2090–2099, while under extreme scenarios, the increase could reach 4.44%. Conclusions: Extreme heat significantly contributes to CVD hospitalizations in Kuwait. This burden is projected to increase under climate change. Findings highlight the urgent need for healthcare system preparedness to mitigate the future health impacts of rising temperatures in Kuwait. [ABSTRACT FROM AUTHOR]

This study evaluates the potential impacts of climate change on Bangladesh by analyzing 19 bioclimatic indicators based on temperature and precipitation. Data from 18 bias-corrected CMIP6 global climate models (GCMs) were used, covering four Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, SSP370, and SSP585—across three future timeframes: near-term (2015–2044), mid-term (2045–2074), and long-term (2075–2100). Under the high-emission SSP585 scenario, average temperatures are projected to rise by up to 3.76 °C, and annual precipitation could increase by 52.6%, reaching up to 3446.38 mm by the end of the century. The maximum temperature (Bio5) could reach 32.91 °C, while the minimum temperature (Bio6) might rise by 4.43 °C, particularly during winter. Precipitation seasonality (Bio15) is projected to increase by as much as 7.9% in the northwest, indicating heightened variability between wet and dry seasons. The diurnal temperature range (Bio2) is expected to decrease by up to − 1.3 °C, signifying reduced nighttime cooling, which could exacerbate heat stress. Significant reductions in temperature seasonality (Bio4) are forecast for the northeast, with notable declines in isothermality (Bio3) under SSP585, pointing to increased climatic extremes. These climatic shifts pose severe risks to agricultural productivity, water resource availability, and biodiversity, particularly in flood-prone regions. The findings highlight the need for urgent adaptation measures, including improved flood management systems, efficient water resource use, and climate-resilient agricultural practices. By providing robust region-specific projections, this study offers critical insights for policymakers and stakeholders to mitigate the adverse effects of climate change and safeguard environmental and economic sustainability in Bangladesh. [ABSTRACT FROM AUTHOR]

The aim of this study is to assess future hazards due to extreme meteorological events in the Wielkopolska region, Poland, based on five climate model projections and three scenarios: SSP126, 370, and 585. The paper analyzes the changes of mean and extreme precipitation, mean and extreme temperatures, and humidity index, as well as changes in difference between maximum temperatures observed from day to day and changes in difference between mean atmospheric pressure at the sea level observed from day to day. Additionally, we look at possible future occurrence of wildfires due to changes in fire weather conditions. Based on climate model projections, future hazard due to extreme meteorological events in Wielkopolska region is to be more serious and will be most noticeable in the end of twenty-first century and for two higher scenarios: SSP370 and SSP585. For near future, 2021–2050, projected conditions of meteorological extremes for analyzed scenarios are quite consistent. Therefore, there is a strong need for implementing adaptation actions. Nevertheless, such activities are so far lacking, and several adaptation options are not present in local and national legislation, even though they are recognized as effective. [ABSTRACT FROM AUTHOR]

In the Andes, the complex topography and unique latitudinal extension of the cordillera are responsible for a wide diversity of climate gradients and contrasts. Part I of this series reviews the current modeling efforts in simulating key atmospheric-orographic processes for the weather and climate of the Andean region. Building on this foundation, Part II focuses on global and regional climate models challenging task of correctly simulating changes in surface-atmosphere interactions and hydroclimate processes to provide reliable future projections of hydroclimatic trajectories in the Andes Cordillera. We provide a review of recent advances in atmospheric modeling to identify and produce reliable hydroclimate information in the Andes. In particular, we summarize the most recent modeling research on projected changes by the end of the 21st century in terms of temperature and precipitation over the Andes, the mountain elevation-dependent warming signal, and land cover changes. Recent improvements made in atmospheric kilometer-scale model configurations (e.g., resolution, parameterizations and surface forcing data) are briefly reviewed, highlighting their impact on modeling results in the Andes for precipitation, atmospheric and surface-atmosphere interaction processes, as mentioned in recent studies. Finally, we discuss the challenges and perspectives of climate modeling, with a focus on the hydroclimate of the Andes. [ABSTRACT FROM AUTHOR]

In this study we analyse projections of future changes to the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) using the latest generation of climate models. Multiple future scenarios are considered. We quantify the fraction of models that project future increases or decreases in the frequency and amplitude of ENSO, IOD, and SAM events in the late 21st century. Changes to the frequency of co‐occurring and consecutive driver phases are also examined. We find that while there is large inter‐model spread, the most common pathways correspond to more frequent ENSO events; weaker, less frequent IOD events; and stronger, but less frequent austral spring SAM events. There is no clear consensus on the change to the frequency of concurrent events, though we find a significant increase in La Niña‐ and El Niño‐only events occurring with neutral IOD and SAM. We also find a significant increase to the frequency of consecutive positive IOD events under a high emissions scenario, but no significant change to the frequency of consecutive ENSO or negative IOD events. In most models, the correlation between drivers, that is, ENSO and IOD, and ENSO and SAM, does not significantly change between the late 20th and late 21st century. These results indicate a high degree of internal variability in the models. Plain Language Summary: Year‐to‐year climate variability impacts ecosystems and billions of people around the world. Major drivers of this variability in the Southern Hemisphere include the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM). As anthropogenic activities continue to impact the climate, and further change is inevitable, we need to improve understanding of how these climate drivers will change. Here we use the latest generation of climate models to investigate how the strength and frequency of these three climate drivers are projected to change over the remainder of the 21st century. We investigate changes to these drivers individually, as well as changes to the frequency of these drivers occurring simultaneously or consecutively. While there is still a large amount of inter‐model spread, the most common model projections are: more frequent ENSO events; weaker and less frequent IOD events; and stronger, but less frequent SAM events. A statistically significant increase in the frequency of consecutive positive IOD events in the high emissions scenario is also found. Key Points: Using all available models and ensemble members, the latest generation of climate models project an overall increase to the frequency of El Niño‐Southern Oscillation (ENSO) events but a weakening and decrease in Indian Ocean Dipole (IOD) events in the late 21st century. The Southern Annular Mode (SAM) is also projected to strengthen in austral springThere is less consensus among models on projections of frequency change of concurrent (co‐occurring) climate driver phases and consecutive ENSO and IOD phasesInternal variability remains a large source of uncertainty. Multi‐model mean results can differ markedly depending on the subset of models used [ABSTRACT FROM AUTHOR]

Abstract Background The Middle East, especially Kuwait, is experiencing rapidly rising temperatures due to climate change. Cardiovascular diseases (CVD) are the leading cause of mortality in the country, and extreme heat is expected to exacerbate hospitalizations for cardiovascular diseases. There is limited data quantifying the historical and future impacts of heat on hospitalizations for cardiovascular diseases in Kuwait. Methods We collected daily hospital admission data of cardiovascular diseases in Kuwait from 2010 to 2019. We modeled the relationship between temperature and cardiovascular disease hospitalizations using distributed lag non-linear models (DLNMs), adjusting for relative humidity and seasonality. Future temperature projections for Kuwait under moderate and extreme climate change scenarios were obtained from the Coupled Model Inter-comparison Project Phase 6 (CMIP6), and the impact on cardiovascular disease hospitalizations was extrapolated for every decade until 2099. Results During the baseline period (2010–2019), a total of 263,182 CVD cases were recorded. Of which, 20,569 (95% eCI: 3,128, 35,757) were attributed to heat. We found that the relative risk of hospitalization for CVD increased from 1.292 (95% CI: 1.051, 1.589) at 41 °C to 1.326 (95% CI: 1.006, 1.747) at 43 °C, compared to the minimum morbidity temperature. Projections showed that, under moderate climate scenarios, CVD hospitalizations would increase by 1.96% by 2090–2099, while under extreme scenarios, the increase could reach 4.44%. Conclusions Extreme heat significantly contributes to CVD hospitalizations in Kuwait. This burden is projected to increase under climate change. Findings highlight the urgent need for healthcare system preparedness to mitigate the future health impacts of rising temperatures in Kuwait.

It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called 'model democracy' by discounting climate models that are too warm over the historical period (i.e., models that 'run hot') when making projections of global temperature change. However, the IPCC did not address whether this procedure is reliable for other quantities. Here, we explore the implications of weighting climate models according to their skill in reproducing historical global‐mean surface temperature using three other climate variables of interest: global average precipitation change, regional average temperature change, and regional average precipitation change. We find that the temperature‐based weighting scheme leads to an improved prediction of global average precipitation, though we show that this prediction could be overconfident. On regional scales, we find a heterogeneous pattern of error reduction in future regional precipitation. This stands in sharp contrast with the broad regional pattern of error reduction in future temperature projections, though we do find regions where error is not significantly reduced. Our results demonstrate that practitioners using weighted climate model ensembles for climate projections must take care when weighting by temperature alone, lest they produce unreliable climate projections that result from an inappropriate weighting procedure. Plain Language Summary: Climate model ensembles are widely used for risk assessment. However, a few of the most recent generation climate models 'run hot' in the historical period, widening the spread of future global warming. The Intergovernmental Panel on Climate Change's (IPCC) sixth assessment report presents a number of weighting schemes to address this 'hot model' problem, each of which discount models that are 'too hot' in the historical period. However, it is unclear if this procedure is reliable for other quantities of interest. Here we explore the impact of this procedure on global average precipitation change, regional temperature change, and regional precipitation change. We find that while this scheme improves the prediction of global precipitation change and generally improves the prediction of regional temperature, it does not broadly improve regional predictions of future precipitation change. We conclude that users of climate model output must be careful when applying a global temperature‐based weighting scheme in regional impact studies. Key Points: Using historical warming to weight climate models can improve global predictions of annual temperature change and precipitation changeUsing past warming to weight future climate projections has varied effects on regional error reduction depending on the metric of interestClimate model end‐users should use caution when applying a weighting scheme to avoid biased or overconfident assessments of climate impacts [ABSTRACT FROM AUTHOR]

Climate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the "practitioner's dilemma." In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the "practitioner's dilemma" for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the "practitioner's dilemma" in the EAR. Plain Language Summary: Groundwater, constituting over one‐third of global water resources, is crucial for sustaining ecosystems, agriculture, and drinking water supplies. In the face of climate change, rising temperatures and shifting precipitation patterns are anticipated to diminish the availability of groundwater for both societal and ecological requirements. Regional managers, in preparing for these changes, need localized climate projections for effective planning. However, the abundance of available climate projections poses a significant challenge for decision‐makers in climate adaptation, known as the "practitioner's dilemma." This dilemma, though widely acknowledged, lacks a standardized solution. Our paper introduces a methodology to navigate this challenge, specifically tailored to the needs of the Edwards Aquifer Authority. This authority is actively engaged in implementing protection and habitat conservation plans to alleviate stress on groundwater and major springs in the Edwards Aquifer Region, located in south‐central Texas. Our projections indicate that rising temperatures are likely to increase evapotranspiration, thereby exacerbating the strain on groundwater resources in this region as climate conditions evolve. Furthermore, our approach offers a customizable approach to "the practitioner's dilemma," potentially serving as a model for other decision‐makers in the United States to effectively utilize climate projections in their strategic planning. Key Points: This study presents a flexible approach to the challenge of selecting climate projections for decision‐makingWe find projected temperature and precipitation changes will stress groundwater resources in the Edwards Aquifer using this approach [ABSTRACT FROM AUTHOR]

Over the last decades observed climate change in Poland had a significant impact on the condition and functioning of the environment. Thus, it is crucial to analyze further future changes to be able to cope with the potential effects of changing climate conditions. In this study, we aimed to assess the impact of projected climate change on meteorological and hydrological conditions in the Biebrza Basin. We analyzed seasonal and annual changes in air temperature, precipitation, streamflow and flood characteristics using the hydrological Soil and Water Assessment Tool (SWAT) model. We examined projected changes for two future time horizons (2024–2050 and 2074–2100) under the Representative Concentration Pathways (RCP) 4.5 and 8.5 using an ensemble of nine EURO CORDEX model scenarios. Climate change projections indicated an increase in precipitation by up to +17 % (+117 mm) and air temperature by up to 3.8 °C by the end of the 21st century. In the analyzed flow gauges a considerable increase in low and mean flows is projected in the future. High flows are projected to slightly decrease for Sztabin, remain at a similar level for Dębowo and slightly increase for Osowiec and Burzyn. Flood area and volume will slightly increase in future horizons. The greatest increase in flood duration (by up to 16 days) is projected for RCP8.5 by the end of the 21st century. It seems, that the expected hydrological conditions, both in the short and long term, will become more stable and improve the conditions for the development of wetlands. [ABSTRACT FROM AUTHOR]

The objective of this work is to provide projections of mean annual and monthly precipitation for the Brazilian Amazon and Cerrado biomes, in the near‐term (2021–2040), medium‐term (2041–2060) and long‐term (2081–2100). The intermediate and most pessimistic Intergovernmental Panel on Climate Change (IPCC) greenhouse gas emissions scenarios were considered. Thus, 34 high‐resolution global climate models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) Phase 6 of the Coupled Model Intercomparison Project (CMIP6) were evaluated. The base period evaluated was from 1981 to 2010. The NEX‐GDDP simulations are bias‐corrected and spatially disaggregated. The Climate Hazards Group InfraRed Precipitation with Station v2.0 was chosen as the source of observed data due to low availability in situ data. The Kling‐Gupta efficiency (KGE) and the global performance indicator were implemented in Google Earth Engine to evaluate the GCMs. The results show that the GCMs perform satisfactorily, except for KACE‐1‐0‐G and IITM‐ESM. The median KGE is 0.86 for the biomes. Thus, the Ensemble Model of 32 GCMs (EM‐32) indicates a reduction in precipitation in the biomes, except the northern Cerrado. In the most pessimistic scenario, changes in annual precipitation range from 3% to −33% until the end of the century. The north‐central Amazon and the northwestern Cerrado are the most affected regions. In general, the monthly precipitations between September and November show the most intense decreasing rates. It is estimated that 91% and 23% of areas in the Amazon and Cerrado biomes, respectively, show robust signs of reduction in mean annual precipitation. Thus, EM‐32 shows more intense and robust climate projections, in comparison to the total annual precipitation of the subset of 33 raw CMIP6 models from Working Group I of the IPCC Sixth Assessment Report. Therefore, the EM‐32 precipitation projections can be applied to future hydrological and hydrosedimentological investigations. [ABSTRACT FROM AUTHOR]

In light of the lack of ground-based observations, this study utilizes reanalysis data from the CHIRPS database and CMIP6 models under the SSP5-8.5 scenario to predict future rainfall in the Beni–Irumu region of eastern DR Congo. The use of reanalysis data offers a viable method for understanding historical and future climate trends in regions with limited ground data. Using a spatial resolution of 0.05°, selected general circulation models (GCMs) were downscaled to CHIRPS data. Analysis of historical rainfall data over 32 years reveals spatial disparities, with high-altitude regions like Mount Stanley experiencing higher annual mean rainfall (1767.87 ± 174.41 mm) compared to lower areas like Kasenyi (863.65 ± 81.85 mm), in line with orographic effects. Future projections under the SSP5-8.5 scenario indicate significant decreases in rainfall for areas such as Oicha (−565.55 mm) in the near term, while regions like Kasindi/Yihunga exhibit moderate decrease (−58.5 mm). In the mid-term, some areas show signs of recovery, with Bulongo experiencing a minor decrease (−21.67 mm), and Kasindi/Yihunga (+152.95 mm) and Kyavinyonge (+71.11 mm) showing increases. Long-term projections suggest overall improvements, with most areas experiencing positive rainfall differences; however, persistent challenges remain in Oicha (−313.82 mm). These findings highlight the dynamic impacts of climate change on rainfall distribution in the Beni–Irumu region, underscoring the need for targeted interventions to address the varied impacts, especially in vulnerable regions like Oicha. [ABSTRACT FROM AUTHOR]

The link between the escalation of global warming and the increase in extreme precipitation events necessitates a deeper understanding of future trends. This study focused on the dynamics of extreme rainfall in Hubei Province throughout the 21st century, a region already sensitive to climatic shifts and extreme weather occurrences. Using the high-resolution global climate model RegCM4 driven by another high-resolution model, HadGEM2-ES, and based on the representative concentration pathway (RCP8.5) emissions scenario, this research predicted the changes in rainfall patterns in Hubei Province during the summer of the 21st century. The accuracy of the adjusted model was confirmed through the use of five extreme rainfall indices (EPIs), namely maximum 5-day amount of precipitation (RX5day), number of heavy rain days (R10), the simple daily intensity index (SDII), consecutive dry days (CDD), and consecutive wet days (CWD), that measured the intensity and frequency of such events. In particular, excluding the index for continuous dry days (CDD), there was an anticipated increase in extreme rainfall during the summer in the mid-21st century. The number of heavy rain days (R10mm) increased significantly (p < 0.05) in the southeastern parts, especially for Wuhan, Xiantao, Qianjiang, Jinzhou, and Ezhou. The EPI values were higher in southeastern Hubei. Consequently, areas such as Wuhan, Xiantao, and Qianjiang in Hubei Province are projected to face more frequent and severe extreme rainfall episodes as the century progresses. [ABSTRACT FROM AUTHOR]

Communicating climate change projections to diverse stakeholders and addressing their concerns is crucial for fostering effective climate adaptation. This paper explores the use of storyline projections as an intermediate technology that bridges the gap between climate science and local knowledge in the Pilcomayo basin. Through fieldwork and interviews with different stakeholders, key environmental concerns influenced by climate change were identified. Traditional approaches to produce regional climate information based on projections often lack relevance to local communities and fail to address their concerns explicitly. By means of storylines approach to evaluate climate projections and by differentiating between upper and middle-lower basin regions and focusing on dry (winter) and rainy (summer) seasons, three qualitatively different storylines of plausible precipitation and temperature changes were identified and related to the main potential risks. By integrating these climate results with local knowledge, a summary of the social and environmental impacts related to each storyline was produced, resulting in three narrated plausible scenarios for future environmental change. The analysis revealed that climate change significantly influences existing issues and activities in the region. Projected trends indicate a shift towards warmer and drier conditions, with uncertainties mainly surrounding summer rainfall, which impacts the probability of increased flooding and river course changes, two of the most concerning issues in the region. These findings serve as a foundation for problem-specific investigations and contribute to informed decision-making for regional climate adaptation. Finally, we highlight the importance of considering local concerns when developing climate change projections and adaptation strategies. [ABSTRACT FROM AUTHOR]

Abstract There is an urgent need for models that can robustly detect past and project future ecosystem changes and risks to the services that they provide to people. The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) was established to develop model ensembles for projecting long‐term impacts of climate change on fisheries and marine ecosystems while informing policy at spatio‐temporal scales relevant to the Inter‐Sectoral Impact Model Intercomparison Project (ISIMIP) framework. While contributing FishMIP models have improved over time, large uncertainties in projections remain, particularly in coastal and shelf seas where most of the world's fisheries occur. Furthermore, previous FishMIP climate impact projections have been limited by a lack of global standardized historical fishing data, low resolution of coastal processes, and uneven capabilities across the FishMIP community to dynamically model fisheries. These features are needed to evaluate how reliably the FishMIP ensemble captures past ecosystem states ‐ a crucial step for building confidence in future projections. To address these issues, we have developed FishMIP 2.0 comprising a two‐track framework for: (a) Model evaluation and attribution of past changes and (b) future climate and socioeconomic scenario projections. Key advances include improved historical climate forcing, which captures oceanographic features not previously resolved, and standardized global fishing forcing to test fishing effects systematically across models. FishMIP 2.0 is a crucial step toward a detection and attribution framework for changing marine ecosystems and toward enhanced policy relevance through increased confidence in future ensemble projections. Our results will help elucidate pathways toward achieving sustainable development goals.

Abstract While deep‐learning downscaling algorithms can generate fine‐scale climate projections cost‐effectively, it is unclear how effectively they extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic Convolutional Neural Network baseline and a Generative Adversarial Network (GAN) built with this baseline, trained to predict daily precipitation simulated by a Regional Climate Model (RCM) over New Zealand. Both approaches emulate future changes in annual mean precipitation well, when trained on historical data, though training on a future climate improves performance. For extreme precipitation (99.5th percentile), RCM simulations predict a robust end‐of‐century increase with future warming (∼5.8%/°C on average from five simulations). When trained on a future climate, GANs capture 97% of the warming‐driven increase in extreme precipitation compared to 65% in a deterministic baseline. Even GANs trained historically capture 77% of this increase. Overall, GANs offer better generalization for downscaling extremes, which is important in applications relying on historical data.

Abstract In this study we analyse projections of future changes to the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Southern Annular Mode (SAM) using the latest generation of climate models. Multiple future scenarios are considered. We quantify the fraction of models that project future increases or decreases in the frequency and amplitude of ENSO, IOD, and SAM events in the late 21st century. Changes to the frequency of co‐occurring and consecutive driver phases are also examined. We find that while there is large inter‐model spread, the most common pathways correspond to more frequent ENSO events; weaker, less frequent IOD events; and stronger, but less frequent austral spring SAM events. There is no clear consensus on the change to the frequency of concurrent events, though we find a significant increase in La Niña‐ and El Niño‐only events occurring with neutral IOD and SAM. We also find a significant increase to the frequency of consecutive positive IOD events under a high emissions scenario, but no significant change to the frequency of consecutive ENSO or negative IOD events. In most models, the correlation between drivers, that is, ENSO and IOD, and ENSO and SAM, does not significantly change between the late 20th and late 21st century. These results indicate a high degree of internal variability in the models.

IntroductionThis paper examines the role of agricultural advisors as key partners for scaling adoption of long-term climate information. Agri-food sectors across the world face significant challenges in responding to climate change, which intersect with broader pressures driving transitions to more climate resilient and sustainable agri-food systems. Making better climate information available to farmers is a key part of responding to these challenges, since relevant and usable climate information can help farmers to adapt to future climate conditions. The development of climate services, which seek to provide climate information to assist with decision making, has therefore increased significantly over the last decade. The Climate Services for Agriculture (CSA) program provides long-term climate projections to help the Australian agriculture sector prepare for and adapt to future climate conditions. ‘My Climate View’ is an online tool produced by CSA, which provides localised and contextualised, commodity-specific climate information, through historic weather data and multi-decadal projections of future climate, aimed at Australian famers and farm advisors. Agricultural advisors have a critical yet often underutilised role as climate information intermediaries, through assisting farmers translate climate information into action.MethodsThis paper uses CSA as a case study to examine farmer-advisor interactions as a key adoption pathway for My Climate View. We interviewed 52 farmers and 24 advisors across Australia to examine the role of advisors as key partners in helping farmers to understand climate information and explore on-farm climate adaptation options.Results and discussionInteractions between farmers and their trusted advisors are an essential part of the enabling environment required to ensure that this long-term climate information can be used at the farm scale to inform longer-term decisions about climate adaptation. We use the concept of an interaction space to investigate farmer-advisor interactions in the adoption and sustained use of My Climate View. We find that although My Climate View is not a transformational technology on its own, its ability to enable farmers and advisors to explore and discuss future climate conditions and consider climate adaptation options has the potential to support transformational changes on-farm that are needed to meet the sustainability transition pressures that climate change presents.

In the Andes, the complex topography and unique latitudinal extension of the cordillera are responsible for a wide diversity of climate gradients and contrasts. Part I of this series reviews the current modeling efforts in simulating key atmospheric-orographic processes for the weather and climate of the Andean region. Building on this foundation, Part II focuses on global and regional climate models challenging task of correctly simulating changes in surface-atmosphere interactions and hydroclimate processes to provide reliable future projections of hydroclimatic trajectories in the Andes Cordillera. We provide a review of recent advances in atmospheric modeling to identify and produce reliable hydroclimate information in the Andes. In particular, we summarize the most recent modeling research on projected changes by the end of the 21st century in terms of temperature and precipitation over the Andes, the mountain elevation-dependent warming signal, and land cover changes. Recent improvements made in atmospheric kilometer-scale model configurations (e.g., resolution, parameterizations and surface forcing data) are briefly reviewed, highlighting their impact on modeling results in the Andes for precipitation, atmospheric and surface-atmosphere interaction processes, as mentioned in recent studies. Finally, we discuss the challenges and perspectives of climate modeling, with a focus on the hydroclimate of the Andes.

Near‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models. Plain Language Summary: Precipitation trends in the southwestern United States (SWUS) are sensitive to the pattern of sea surface temperature (SST) trends in the Tropical Pacific. Since the turn of the century, a decrease in SWUS precipitation has been linked to a cooling of the Central and Eastern Pacific (1990–2020). Notably, climate models are unable to simulate this observed cooling SST trend. In this study, we answer how SWUS precipitation projections may be impacted by potential error in the simulation of future SST trends by climate models. We first demonstrate that slight changes in the pattern of SST trends leads to either a wetting or drying of the SWUS. Second, if the current 30‐year cooling trend in the Central and East Pacific switches to a warming trend, the SWUS could experience a near‐term increase in precipitation. While climate models are the main tool to predict the global response to anthropogenic climate change, we must consider and account for their error in projections of global warming. Key Points: The observed Equatorial Pacific cooling trend, unpredicted by climate models, may have led to decreased precipitation in the southwestern USWith a sea surface temperature‐precipitation Green's function, we find that small changes in sea surface temperature can either wet or dry the southwestern USA reversal of the cooling trend in the Equatorial Pacific could lead to a wetting trend in the southwestern US [ABSTRACT FROM AUTHOR]

Unquestionably, the rapidly changing climate and, therefore, alterations in the associated bioclimate, constitute an alarming reality with implications for daily practice and natural capital management. This research displays the present and projected bioclimate evolution over Greece's phytogeographical regions. For this purpose, ultrahigh-resolution computation results on the spatial distribution of the Emberger index's Q2 classes of bioclimatic characterization are analyzed and illustrated for the first time. The assessments are performed over the reference period (1970–2000) and two future time frames (2021–2040; 2041–2060) under the RCP4.5 and RCP8.5 emission scenarios. By 2060 and under the extreme RCP8.5, intense xerothermic trends are demonstrated owing to the resulting significant spatial evolution mainly of the Arid–Hot, Semi-Arid–Very Hot, Semi-Arid–Hot, and Semi-Arid–Temperate Q2 classes, respectively, over the phytogeographical regions of Kiklades (up to 29% occupation), Kriti and Karpathos (up to 30%), West Aegean Islands (up to 26%), North East (up to 56%), and North Central (up to 31%). The RCP8.5 long-term period exhibits the strongest impacts over approximately the right half of the Greek territory, with the bioclimate appearing more dry–thermal in the future. In conclusion, the Emberger index provides an in-depth view of the Greek area's bioclimatic regime and the potential alterations due to climate change per phytogeographical region. [ABSTRACT FROM AUTHOR]

Climate change is causing a range of environmental impacts, including increased flood frequency and intensity, posing significant risks to human populations and transportation infrastructure. Assessing flood risk under climate change is critical, but it is challenging due to uncertainties associated with climate projections and the need to consider the interactions between different factors that influence flood risk. Geographic Information Systems (GISs) are powerful tools that can be used to assess flood risk under climate change by gathering and integrating a range of data types and sources to create detailed maps of flood-prone areas. The primary goal of this research is to create a comprehensive GIS-based flood risk map that includes various climate change scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This goal will leverage the Analytic Hierarchy Process (AHP) methodology to better understand the impacts of these climate change scenarios on the transportation network. Furthermore, this study aims to evaluate the existing flood risk map and assess the potential impacts of prospective climate scenarios on the levels of flood risk. The results showed that the northern and coastal regions of the United Arab Emirates (UAE) are at higher risk of flooding, with the majority of the population living in these areas. The projections for future flood risk levels indicate that under the SSP245 scenario, flood risk levels will generally be low, but some areas in the northern and eastern regions of the UAE may still face high to very high flood risk levels due to extensive urbanization and low-lying coastal regions. Under the SSP585 scenario, flood risk levels are projected to be significantly higher, with a widespread distribution of very high and high flood risk levels across the study area, leading to severe damage to infrastructure, property, and human lives. The recent publication of the CMIP6 models marks a significant advancement, and according to the authors' knowledge, there have been no studies that have yet explored the application of CMIP6 scenarios. Consequently, the insights provided by this study are poised to be exceptionally beneficial to researchers globally, underscoring the urgent necessity for holistic sustainable flood risk management approaches for geography, planning, and development areas. These approaches should integrate both sustainable transportation infrastructure development and risk mitigation strategies to effectively address the anticipated impacts of flooding events within the study region. [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are <0.5°C and biases in the 1‐in‐1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR‐ESDM represents a significant advance in generating consistent high‐resolution projections to comprehensively assess climate risk and optimize resilience globally. Plain Language Summary: The STAR‐ESDM tool is able to quickly and accurately generate future climate projections for weather stations and high‐resolution grids anywhere in the world. It does this by breaking down global or regional climate model output into different components, from the long‐term trend to the day‐to‐day variability, then merging projected changes with observations. When tested against projections generated by a complex and computationally expensive dynamical global model, STAR‐ESDM produced almost the same output, even for extreme temperature and precipitation values, at a fraction of the computational cost. Moreover, unlike most statistical downscaling models, this method isn't tied to any specific geographic area or predictand and/or predictor data set. It can be applied to any regional or global data set, whether generated by a climate or reanalysis model, derived from satellite observations, recorded at weather stations, and more. As climate impacts escalate, STAR‐ESDM offers a flexible and effective way to generate the high‐resolution climate projections needed to better gauge climate risk and enhance resilience anywhere in the world where reliable observational or quasi‐observational data, including reanalysis or satellites, are available. This is particularly relevant to under‐resourced regions, which are often most vulnerable to climate impacts as well as most lacking in future projections. Key Points: STAR‐ESDM is a rapid, flexible and generalizable approach to bias‐correct and downscale climate model output to any finer‐resolution data setPredictors/predictands can be derived from global or regional models, satellites, reanalysis, gridded observations and weather stationsProjected changes in temperature and precipitation mirror those of a high‐resolution global model at a fraction of the computational cost [ABSTRACT FROM AUTHOR]

Background: Climate projections signal longer fire seasons and an increase in the number of dangerous fire weather days for much of the world including Australia. Aims: Here we argue that heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought will amplify potential fire behaviour well beyond projections based on calculations of afternoon forest fire danger derived from climate models. Methods: We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves, drawing on examples of dynamical processes driving fire behaviour during the Australian Black Summer bushfires of 2019–20. Results: Key dynamical processes identified include: nocturnal low-level jets, deep, unstable planetary boundary layers and fire–atmosphere coupling. Conclusions: The future scenario we contend is long windows of multi-day fire events where overnight suppression is less effective and fire perimeters will expand continuously and aggressively over multiple days and nights. Implications: Greater overnight fire activity and multi-day events present strategic and tactical challenges for fire management agencies including having to expand resourcing for overnight work, manage personnel fatigue and revise training to identify conditions conducive to unusually active fire behaviour overnight. Effective messaging will be critical to minimise accidental fire ignition during heatwaves and to alert the community to the changing fire environment Heatwaves, dynamic fire–atmosphere interactions and increased fuel availability caused by drought are likely to amplify fire behaviour under climate change. We review meteorological dynamics contributing to enhanced fire behaviour during heatwaves using examples from the 2019–20 Australian Black Summer bushfires and examine potential challenges posed for future fire management. [ABSTRACT FROM AUTHOR]

We present a framework for developing storylines of UK sea level rise to aid risk communication and coastal adaptation planning. Our approach builds on the UK national climate projections (UKCP18) and maintains the same physically consistent methods that preserve component correlations and traceability between global mean sea level (GMSL) and local relative sea level (RSL). Five example storylines are presented that represent singular trajectories of future sea level rise drawn from the underlying large Monte Carlo simulations. The first three storylines span the total range of the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6) likely range GMSL projections across the SSP1-2.6 and SSP5-8.5 scenarios. The final two storylines are based upon recent high-end storylines of GMSL presented in AR6 and the recent literature. Our results suggest that even the most optimistic sea level rise outcomes for the UK will require adaptation of up to 1 m of sea level rise for large sections of coastline by 2300. For the storyline most consistent with current international greenhouse gas emissions pledges and a moderate sea level rise response, UK capital cities will experience between about 1 and 2 m of sea level rise by 2300, with continued rise beyond 2300. The storyline based on the upper end of the AR6 likely range sea level projections yields much larger values for UK capital cities that range between about 3 and 4 m at 2300. The two high-end scenarios, which are based on a recent study that showed accelerated sea level rise associated with ice sheet instability feedbacks, lead to sea level rise for UK capital cities at 2300 that range between about 8 m and 17 m. These magnitudes of rise would pose enormous challenges for UK coastal communities and are likely to be beyond the limits of adaptation at some locations. [ABSTRACT FROM AUTHOR]

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Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070-2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June-August), with the smallest increases predicted for the last quarter (December-February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies. [ABSTRACT FROM AUTHOR]

The intricate relationship between climate and society in a given region demands a profound understanding of climate patterns, especially in agricultural areas like Diamantina, Minas Gerais (MG), recognized by the Food and Agriculture Organization (FAO) as the birthplace of the first Globally Important Agricultural Heritage System (GIAHS) in Brazil, situated in the southwest region of the country. Given the growing concerns about climate change, we conducted a meticulous analysis of the climatic characteristics of Diamantina-MG. To achieve this, we examined historical meteorological data from 1973 to 2022, employing the Mann–Kendall and Sen's slope tests to analyze trends. Additionally, we utilized three global climate models (GCMs) under different shared socioeconomic pathways (SSPs) to predict future climate scenarios (2021–2100) based on the projections of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Furthermore, we used Köppen and Thornthwaite climate classification methodologies to characterize both the current and future climate conditions of the region. Our results indicate that, historically, Diamantina-MG has experienced significant increases in minimum temperature, indicating a warmer climate in recent decades. For temperature, the projections show a consensus among models, projecting a continuous increase, potentially reaching up to 5.8 °C above the historical average temperature (19.2 °C) by the end of the century. Regarding rainfall projections, they show greater uncertainty, with discrepancies among models observed until 2060. However, specifically for the second half of the century (2060–2100), the models agree that there will be increases in annual rainfall. Regarding the climatic types of the region, we found that the current Köppen Cwb and Thornthwaite B3rB'3a' classifications could shift to Aw and B1wA'a', representing a humid tropical savanna climate with longer periods of water deficiency, considering the impacts resulting from increased air temperature and evapotranspiration. In summary, the study's results indicate that climate changes are occurring and are likely to intensify in the Jequitinhonha Valley region, MG, in the future. The analysis of these data, from the perspective of the Brazilian GIAHS sustainability, reveals the importance of considering adaptation and mitigation measures to ensure the resilience of agricultural systems and local communities in the region that face these significant environmental changes. [ABSTRACT FROM AUTHOR]

Japanese plum, like other temperate fruit tree species, has cultivar-specific temperature requirements during dormancy for proper flowering. Knowing the temperature requirements of this species is of increasing interest due to the great genetic variability that exists among the available Japanese plum-type cultivars, since most of them are interspecific hybrids. The reduction of winter chilling caused by climate change is threatening their cultivation in many regions. In this work, the adaptation perspectives of 21 Japanese plum-type cultivars were analyzed in two of the main plum-growing regions in Spain, Badajoz and Zaragoza, to future climate conditions. Endodormancy release for subsequent estimation of chilling and heat requirements was determined through empirical experiments conducted during dormancy at least over two years. Chill requirements were calculated using three models [chilling hours (CH), chilling units (CU) and chilling portions (CP)] and heat requirements using growing degree hours (GDH). Chilling requirements ranged 277-851 CH, 412-1,030 CU and 26-51 CP, and heat requirements ranged from 4,343 to 9,525 GDH. The potential adaption of the cultivars to future warmer conditions in both regions was assessed using climate projections under two Representative Concentration Pathways (RCP), RCP4.5 (effective reduction of greenhouse gas emissions) and RCP8.5 (continuous increase in greenhouse gas emissions), in two time horizons, from the middle to the end of 21st century, with temperature projections from 15 Global Climate Models. The probability of satisfying the estimated cultivarspecific chilling requirements in Badajoz was lower than in Zaragoza, because of the lower chill availability predicted. In this region, the cultivars analyzed herein may have limited cultivation because the predicted reduction in winter chill may result in the chilling requirements not being successfully fulfilled. [ABSTRACT FROM AUTHOR]

Abstract It depends. The Intergovernmental Panel on Climate Change's (IPCC) Assessment Report Six (AR6) took a step toward ending so‐called ‘model democracy’ by discounting climate models that are too warm over the historical period (i.e., models that ‘run hot’) when making projections of global temperature change. However, the IPCC did not address whether this procedure is reliable for other quantities. Here, we explore the implications of weighting climate models according to their skill in reproducing historical global‐mean surface temperature using three other climate variables of interest: global average precipitation change, regional average temperature change, and regional average precipitation change. We find that the temperature‐based weighting scheme leads to an improved prediction of global average precipitation, though we show that this prediction could be overconfident. On regional scales, we find a heterogeneous pattern of error reduction in future regional precipitation. This stands in sharp contrast with the broad regional pattern of error reduction in future temperature projections, though we do find regions where error is not significantly reduced. Our results demonstrate that practitioners using weighted climate model ensembles for climate projections must take care when weighting by temperature alone, lest they produce unreliable climate projections that result from an inappropriate weighting procedure.

Abstract Climate projections are being used for decision‐making related to climate mitigation and adaptation and as inputs for impacts modeling related to climate change. The plethora of available projections presents end users with the challenge of how to select climate projections, known as the “practitioner's dilemma.” In addition, if an end‐user determines that existing projections cannot be used, then they face the additional challenge of producing climate projections for their region that are useful for their needs. We present a methodology with novel features to address the “practitioner's dilemma” for generating downscaled climate projections for specific applications. We use the Edwards Aquifer region (EAR) in south‐central Texas to demonstrate a process to select a subset of global climate models from both the CMIP5 and CMIP6 ensembles, followed by downscaling and verification of the accuracy of downscaled data against historical data. The results show that average precipitation changes range from a decrease of 10.4 mm to an increase of 25.6 mm, average temperature increases from 2.0°C to 4.3°C, and the number of days exceeding 37.8°C (100°F) increase by 35–70 days annually by the end of century. The findings enhance our understanding of the potential impacts of climate change on the EAR, essential for developing effective regional management strategies. Additionally, the results provide valuable scenario‐based projected data to be used for groundwater and spring flow modeling and present a clearly documented example addressing the “practitioner's dilemma” in the EAR.

Climate change is known to increase the frequency and intensity of hot days (daily maximum temperature ≥30°C), both globally and locally. Exposure to extreme heat is associated with numerous adverse human health outcomes. This study estimated the burden of heat-related illness (HRI) attributable to anthropogenic climate change in North Carolina physiographic divisions (Coastal and Piedmont) during the summer months from 2011 to 2016. Additionally, assuming intermediate and high greenhouse gas emission scenarios, future HRI morbidity burden attributable to climate change was estimated. The association between daily maximum temperature and the rate of HRI was evaluated using the Generalized Additive Model. The rate of HRI assuming natural simulations (i.e., absence of greenhouse gas emissions) and future greenhouse gas emission scenarios were predicted to estimate the HRI attributable to climate change. Over 4 years (2011, 2012, 2014, and 2015), we observed a significant decrease in the rate of HRI assuming natural simulations compared to the observed. About 3 out of 20 HRI visits are attributable to anthropogenic climate change in Coastal (13.40% [IQR: -34.90,95.52]) and Piedmont (16.39% [IQR: -35.18,148.26]) regions. During the future periods, the median rate of HRI was significantly higher (78.65%: Coastal and 65.85%: Piedmont), assuming a higher emission scenario than the intermediate emission scenario. We observed significant associations between anthropogenic climate change and adverse human health outcomes. Our findings indicate the need for evidence-based public health interventions to protect human health from climate-related exposures, like extreme heat, while minimizing greenhouse gas emissions.

The Forest Fire Danger Index (FFDI) is the main measure used in Australia for estimating fire risk. Recent work by the authors showed that the FFDI forms stable state regimes, nominated as fire climate regimes. These regimes shifted to greater intensity in southern and eastern Australia around the year 2000 and, a decade later, further north. Reductions in atmospheric moisture were the primary contributor. These changes have not been fully incorporated into future projections. This paper compares the recent regime shifts with the most recent national projections of FFDI, published in 2015. They show that for most states and regions, the 2030 upper limit is approached or exceeded by the recent shift, except for two states with large arid zones, South Australia and Western Australia. Methods for attributing past changes, constructing projections, and the inability of climate models to reproduce the recent decreases in atmospheric moisture, all contribute to these underestimates. To address these shortcomings, we make some suggestions to modify efforts aiming to develop seamless predictions and projections of future fire risk. [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally. Plain Language Summary: Northeast China (NEC) is essential for achieving food accessibility and food security in China, but its agricultural production is seriously threatened by climate change. This study investigated how climate change could impact maize, rice, and soybean crop yields in this region from 2015 to 2100, using the PCSE crop model and forcing from the international Coupled Model Intercomparison Project 6 (CMIP6) climate projections. Simulation results suggested rising temperatures would negatively affect crop yields, especially for maize. Increased carbon dioxide and rainfall partially offset these losses. By 2100, average declines were projected for maize (−21%) and soybeans (−4%), respectively. Altering planting dates to match crop needs was an effective adaptation to boost yields, with rice benefiting the most. Relocating rice production eastward and expanding its area substantially increased simulated rice yields (+62%) but decreased other crops due to competition (−32% maize, −28% soybeans). Even with adaptations, some areas still showed yield declines, indicating extra measures may be needed to maintain production. Overall, this study suggests significant risks to crop yields from climate change in Northeast China, with adaptations like optimized planting and strategic crop relocation able to mitigate some but not all of the projected impacts. Key Points: Rising temperatures negatively impacted simulated future crop yields, especially for maizeAdjusting planting dates boosted yields for rice and soybeans but had limited benefits for maizeRelocating and expanding the rice area increased yields substantially [ABSTRACT FROM AUTHOR]

Climate change has been shown to alter the spatial distribution of whales and other marine mammals. Fast changing ocean temperatures may also affect the spatial distribution of whales at a finer scale, namely within populations, including aggregation behaviour. Our ability to analyze the impact of climate change on whale aggregation behavior, however, has been limited by our ability to collect spatial observation data over large areas. To overcome this limitation, this study analyzed open-access satellite imagery obtained between 2007 and 2020 in Canada, Russia, and Alaska using Deep Convolutional Neural Networks (CNN) to detect 1,980 beluga whales in 11 populations and to quantify their aggregation patterns within their populations. Subsequently, we examined the relationship between sea surface temperature (SST) and the intra-population spatial patterns of beluga whales during summer seasons, when these whales normally aggregate. We detected a negative correlation between SST and the frequency of beluga whale aggregation, suggesting that warming temperatures may impact beluga whale spatio-behavioral dynamics. Considering that the relative abundance of beluga whales is declining and the future SST projections in these Arctic Ocean locations, climate change may pose yet another threat to beluga whales and other ice-dependent species. [ABSTRACT FROM AUTHOR]

Climate change interacts with ecological processes leading to changes in tree and forest growth rate, biome shifts and species composition, all of which are influenced by disturbances. This study explores future overarching climate trends of eight of Canada's ecozones containing managed forests. For the 2071 to 2100 period, climate projections indicate a warming trend of up to an additional 5.5 °C and an overall increase in annual precipitation. Future trends suggest marked contrast between coastal and interior forests and polarization between western and eastern forests. Warmer temperatures, accumulating degree-days above 5 °C and frost-free days suggest longer and drier growing seasons and greater risk of drought particularly in moisture-limited areas such as montane cordillera, taiga shield and boreal shield ecozones. Warmer temperatures and rising precipitation combined with less snow suggest shorter and wetter future winters. This indicates greater risk of rain-on-snow and freeze-thaw events, flooding and landslides particularly in coastal ecozones. We discuss how these projections are likely to result in shifts in dominant species and abundance, which when coupled with the cumulative effects of future disturbances, is likely to alter future forest dynamics and impact harvestable wood volumes for Canada's forestry industry. [ABSTRACT FROM AUTHOR]

Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile's climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models' projections for the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called "hot models" likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6 °C in SSP585, followed by Central Chile (up to ~ 5 °C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4 °C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile. [ABSTRACT FROM AUTHOR]

Climate models predict meteorological variables for outdoor spaces. Nevertheless, most people work indoors and are affected by heat indoors. We present an approach to transfer climate projections from outdoors to climate projections of indoor air temperature (Ti) and thermal comfort based on a combination of indoor sensors, artificial neural networks (ANNs), and 22 regional climate projections. Human thermal comfort and Ti measured by indoor sensors at 90 different workplaces in the Upper Rhine Valley were used as training data for ANN models predicting indoor conditions as a function of outdoor weather. Workplace-specific climate projections were modeled for the time period 2070–2099 and compared to the historical period 1970–1999 using the same ANNs, but ERA5-Land reanalysis data as input. It is shown that heat stress indoors will increase in intensity, frequency, and duration at almost all investigated workplaces. The rate of increase depends on building and room properties, the workplace purpose, and the representative concentration pathway (RCP2.6, RCP4.5, or RCP8.5). The projected increase of the mean air temperature in the summer (JJA) outdoors, by + 1.6 to + 5.1 K for the different RCPs, is higher than the increase in Ti at all 90 workplaces, which experience on average an increase of + 0.8 to + 2.5 K. The overall frequency of heat stress is higher at most workplaces than outdoors for the historical and the future period. The projected hours of indoor heat stress will increase on average by + 379 h, + 654 h, and + 1209 h under RCP2.6, RCP4.5, and RCP8.5, respectively. [ABSTRACT FROM AUTHOR]

The latest assessment report of the Intergovernmental Panel on Climate Change (IPCC) provided scenario‐based local sea level projections to 2150 and characterized the long‐term committed global mean sea level rise on 2,000‐ and 10,000‐year time horizons associated with peak surface warming levels. Turner et al. build on the scientific assessment of the IPCC to provide time‐continuous projections of future sea level rise to 2500. These projections fill an important knowledge gap to help inform coastal decision‐making processes and more fully quantify the benefits of mitigation actions in terms of limiting future sea level rise. However, limited understanding of ice instability processes remains a key scientific challenge and improved observational and modeling capability are critical to reducing uncertainties and monitoring the trajectory of observed change. Plain Language Summary: Sea level rise presents a major societal challenge for coastal communities and decision‐makers around the world. Due to the slow response of the oceans and ice sheets to global warming, sea level rise will continue for many centuries even under scenarios with strong reductions in future greenhouse gas emissions. In this issue Turner et al. provide a pioneering study to estimate the sea level rise to 2500 to support coastal adaptation planning and demonstrate the long‐term benefits of reductions in greenhouse gas emissions. Key Points: Multi‐century sea level projections represent a key knowledge gap that are vital for long‐term coastal planningTurner et al. present new sea level projections to 2500 that represent a important step toward addressing this needTheir work also illustrates the benefits of mitigation action for limiting future sea level rise [ABSTRACT FROM AUTHOR]

Abstract Near‐term projections of drought in the southwestern United States (SWUS) are uncertain. The observed decrease in SWUS precipitation since the 1980s and heightened drought conditions since the 2000s have been linked to a cooling sea surface temperature (SST) trend in the Equatorial Pacific. Notably, climate models fail to reproduce these observed SST trends, and they may continue doing so in the future. Here, we assess the sensitivity of SWUS precipitation projections to future SST trends using a Green's function approach. Our findings reveal that a slight redistribution of SST leads to a wetting or drying of the SWUS. A reversal of the observed cooling trend in the Central and East Pacific over the next few decades would lead to a period of wetting in the SWUS. It is critical to consider the impact of possible SST pattern trends on SWUS precipitation trends until we fully trust SST evolution in climate models.

Abstract High‐resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR‐ESDM) is a computationally‐efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long‐term predictor trends are bias‐corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high‐resolution bias‐corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR‐ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high‐resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1‐in‐1000 hottest and coldest temperatures are

Ecuador, a country with distinct coastal (CO), highland (HL), and Amazon (AM) regions that are characterized by unique climatic, ecological, and socio-economic features is highly vulnerable to climate change. This study focuses on these three regions, highlighting their individual importance in the broader context of Ecuador's climate vulnerability. Utilizing dynamically downscaled data from the Regional Climate Model (RCM), we generated precipitation and air temperature projections for the period 2070–2099 under three different climate change scenarios. We indicate projected temperature increases across all three regions: mean temperature increases for the CO, HL and AM regions are of 1.35, 1.55, and 1.21°C, respectively. Each year, the largest temperature increases are predicted for the third quarter (June–August), with the smallest increases predicted for the last quarter (December–February). Precipitation patterns show varied changes, with CO exhibiting a positive mean daily change, in contrast to a mean negative change in the AM region. These region-specific projections underscore the differential impacts of climate change within Ecuador and highlight the necessity for tailored adaptation measures. The study's novel approach, focusing on distinct regional impacts within a single nation, offers valuable insights for policymakers, aiding in the development of effective, region-specific climate change mitigation and adaptation strategies. This targeted approach is crucial to address unique challenges faced by different regions, thereby supporting national resilience strategies.