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It is crucial to investigate the characteristics of fire danger in the areas around Beijing to increase the accuracy of fire danger monitoring, forecasting, and management. Using meteorological data from 17 national meteorological stations in the areas around Beijing from 1981–2021, this study calculated the fire weather index (FWI) and analyzed its spatiotemporal characteristics. It was found that the high and low fire danger periods were in April–May and July–August, with spatial patterns of "decrease in the northwest–increase in the southeast" and a significant increase throughout the areas around Beijing, respectively. Next, the contributions of different meteorological factors were quantified by the multiple regression method. We found that during the high fire danger period, the northern and southern parts were affected by precipitation and minimum relative humidity, respectively. However, most areas were influenced by wind speed during the low fire danger period. Finally, comparing with the FWI characteristics under different SSP scenarios, we found that the FWI decreased during high fire danger period and increased during low fire danger period under different SSP scenarios (i.e., SSP245, SSP585) for periods of 2021–2050, 2071–2100, 2021–2100, except for SSP245 in 2071–2100 with an increasing trend both in high and low fire danger periods. This study implies that there is a higher probability of FWI in the low fire danger period, threatening the ecological environment and human health. Therefore, it is necessary to enhance research on fire danger during the low fire danger period to improve the ability to predict summer fire danger. [ABSTRACT FROM AUTHOR]

Abstract Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa.

Climate change leading to Climate extremes in the twenty-first century is more evident in megacities across the world, especially in West Africa. The Greater Accra region is one of the most populated regions in West Africa. As a result, the region has become more susceptible to climate extremes such as floods, heatwaves, and droughts. The study employed the Coupled Model Intercomparison Project 6 models in simulating climate extreme indices under the Shared Socioeconomic Pathway scenarios (SSPs) over West Africa between 1979 and 2059 as exemplified by the Greater Accra region. The study observed a generally weak drought in the historical period and expected to intensify especially under SSP585 in Greater Accra. For instance, continuous dry days (CDD) reveal an increasing trend under the SSPs. Similarly, the overall projected trend of CDD over West Africa reveals an increase signifying a more frequent and longer drought in the future. The flood indices revealed a surge in the intensity and duration of extreme precipitation events under the SSPs in the region. For instance, R99pTOT and Rx5days are expected to significantly increase under the SSPs with intensification under the SSP245, SSP370, and SSP585. A similar trend has been projected across West Africa, especially along the Guinean coast. The study foresees a gradual and intensifying rise in heatwave indices over the Greater Accra region. The warming and cooling indices reveal an increasing and decreasing trend respectively in the historical period as well as under the SSPs particularly within urban centers like Accra and Tema. Most West African countries are projected to observe more frequent warm days and nights with cold nights and days becoming less frequent. Expected effects of future climate extreme indices pose potential threats to the water, food, and energy systems as well as trigger recurrent floods and droughts over Greater Accra. The findings of the study are expected to inform climate policies and the nationally determined contribution of the Paris Agreement as well as address the sustainable development goal 11 (Sustainable cities) and 13 (Climate action) in West Africa. [ABSTRACT FROM AUTHOR]

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world's largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan's heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country. [ABSTRACT FROM AUTHOR]

Global aquaculture production is steadily rising, and the aquaculture industry is assuming an increasingly vital role in economic aspects. Despite the growing importance of aquaculture on a global scale, climate change poses challenges to the future growth and stability of the aquaculture. In South Korea, marine aquaculture production is gradually increasing; however, higher sea temperatures caused by climate change are inflicting significant damage on the aquaculture industry. Accordingly, it is necessary to take measures to adapt to climate change, and assessing the level of risks facing the aquaculture from climate change is crucial as the first step in this process. In this study, we conducted the risk assessment to climate change for seventeen primary aquaculture species with sensitivity and impact attributes. As an impact attribute, we consider relationship between projected sea surface temperature and optimal and tolerable temperature by species to enable a more objective and quantitative assessment. The assessed risk scores were re-evaluated based on three criteria of the traffic light system to verify fish farming likelihoods by region. Based on the results, aquaculture was expected to face challenges in multiple areas in the future under the SSP5-8.5. Among the species, seaweed and sea squirt would become unviable for aquaculture in production regions in the future, highlighting the need for prioritized adaptation measures compared to other aquaculture species. [ABSTRACT FROM AUTHOR]

The present study aims to determine the current distribution areas of Fagus orientalis Lipsky and the changes in suitable distribution areas due to global climate change within the borders of Kastamonu Forest Regional Directorate of Forestry. In the present study, the current suitable distribution areas of Fagus orientalis and the suitable distribution areas in the years 2040, 2070, and 2100 according to socioeconomic pathway 126, socioeconomic pathway 370, and socioeconomic pathway 585 scenarios were determined. The study results indicate that the suitable distribution areas of Fagus orientalis populations in Kastamonu will change depending on the effects of climate change and that the change will be in the form of an increase in general. [ABSTRACT FROM AUTHOR]

Research area Sivas province and the subject of the research is average temperature and total precipitation parameters and SSP scenarios. The study aims to analyze the average temperature and precipitation data, and analyze these data in annual and seasonal analyzing the trends and predictions for the region in the coming years (2060-2100) to determine the possible temperature and precipitation conditions. Two important trend methods such as Mann-Kendall and Spearman's Rho test were used in the study. Climate SSP 2.6 and 8.5 were preferred in the scenarios. According to the results obtained increases in annual and seasonal average temperatures across the province were observed. These increases were statistically significant at some stations. while it is not significant in some stations. It is seen that summer in Sivas province is the season that shows a positive and significant temperature increase trend. season. In summer, autumn and winter seasons and annually in the province It was observed that there were increases and decreases in precipitation. As a result, trend analyzes indicate long-term temperature increases and precipitation decreases in Sivas province in terms of global climate changes. [ABSTRACT FROM AUTHOR]

Background and Objectives: Global warming cause changes in temperature variables and consequently increase in the occurrence of extreme events like floods and droughts, result from extreme weather in most cases; Therefore, it is important to study and investigate the future changes of extreme values of meteorological and hydrological parameters, including temperature, more than the average or median value. Therefore, the purpose of this research is to investigate the effects of climate change on the trend of seasonal changes in extreme temperatures (very high and very low values) in coastal stations of Mazandaran province based on CMIP6 models and quantile regression method. Materials and Methods: In this research, in order to investigate the trend of extreme values of temperature in Mazandaran province for the future periods, the output of the sixth generation of climate change models (Coupled Model Intercomparison Project phase 6, CMIP6) was used. For this purpose, minimum and maximum temperature of four main synoptic stations of the province, including Babolsar, Qarakhil, Ramsar and Nowshahr, and also the output of the NorESM2-MM climate model from the CMIP6 in near future (2026-2055) and far future (2071-2100) at three optimistic (SSP126), moderate (SSP245) and pessimistic (SSP585) scenarios was used. In order to downscale climate model data at the studied meteorological stations, various methods in the CMhyd downscaling software were used, and the temperature data output for the method that had higher accuracy, was selected for analysis of trend. In the next step, the quantile regression method was used to investigate the seasonal trend of temperature extreme values and the results were analyzed. Results: The results of downscaling using different methods have shown that the Variance Scaling method has the best performance among the available methods in CMhyd. In general, the results show a positive temperature anomaly (annual temperature increase compared to the base period in SSP126=1.3, SSP245=2.56, SSP585=3.2 °C) in Mazandaran province in all months of the year until the end of the 21st century. The intensity of the anomalies is higher in the warm months than in the cold ones. Under the optimistic scenario, the extreme values of the temperature variables in the spring and autumn seasons will decrease by a maximum of 1 degree per decade in the distant future. However, the average minimum temperature is increasing in all scenarios (including the optimistic scenario) and the decrease is only predicted for extreme values. Under the average and pessimistic scenarios, there will be an increase in the temperature extreme values in all seasons, so that its intensity will be greater for the pessimistic scenario and in the spring season (0.15 °C per year). Conclusion: According to the results, it concluded that the significant increase in extreme temperature during the day and night, especially in the warm months of the year, will increase evapotranspiration, and along with the decrease in rainfall in the hot months, it will cause a decrease in water resources in different parts of Mazandaran province and pressure on the groundwater. Therefore, it is very important to formulate and implement appropriate management programs according to the needs of each region, in order to adapt to extreme temperatures and their adverse consequences. [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China's largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people's daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase. [ABSTRACT FROM AUTHOR]

North Africa is acknowledged as one of the world's hottest and most vulnerable regions to climate change. This study addresses a critical gap in comprehensive research on climate change in Algeria, focusing specifically on the Setif high plains in Northeast Algeria. Despite its susceptibility, studies on this region remain scarce. Utilizing the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset and five emission scenarios (SSPs), this research projects climate changes during the twenty-first century (2030–2050–2070). Results indicate significant temperature increases and precipitation declines. SSP5-5.8 presents the most pessimistic outlook, foreseeing the highest temperature elevations (24.52 to 27.18°C, 31.83 to 34.43°C, and 17.30 to 20°C for average, maximum, and minimum temperatures), and the largest precipitation decreases (65.9% to 60.94%). Conversely, SSP1-1.9 offers a more optimistic perspective with smaller temperature increments (24.21 to 24.36°C, 31.55 to 31.69°C, and 17.01 to 17.10°C) and a milder precipitation reduction (68.07% to 71.97%). These findings underscore a warming trend and reduced precipitation, emphasizing the urgent need for understanding climate change impacts in the region. Addressing these challenges is crucial for devising effective adaptation and resilience measures. The agricultural sector, a vital aspect of the region's economy, may benefit from adjustments such as reducing rainfed crop cultivation and adopting intensive farming practices. Moreover, advancements in biotechnology and climate predictions offer promising avenues for adapting agricultural water management to the changing climate. This research not only contributes to the knowledge base on climate change but also provides actionable insights for policymakers and practitioners, aiding in the development of sustainable strategies to mitigate risks and foster resilience in the face of a changing climate. [ABSTRACT FROM AUTHOR]

Phenological models are built upon an understanding of the influence of environmental factors on plant phenology, and serve as effective tools for predicting plant phenological changes. However, the differences in phenological model predictive performance under different climate change scenarios have been rarely studied. In this study, we parameterized thirteen spring phenology models, including six one-phase models and seven two-phase models, by combining phenological observations and meteorological data. Using climatic data from two Shared Socioeconomic Pathways (SSP) scenarios, namely SSP126 (high mitigation and low emission) and SSP585 (no mitigation and high emission), we predicted spring phenology in Germany from 2021 to 2100, and compared the impacts of dormancy phases and driving factors on model predictive performance. The results showed that the average correlation coefficient between the predicted start of growing season (SOS) by the 13 models and the observed values exceeded 0.72, with the highest reaching 0.80. All models outperformed the NULL model (Mean of SOS), and the M1 model (driven by photoperiod and forcing temperature) performed the best for all the tree species. In the SSP126 scenario, the average SOS advanced initially and then gradually shifted towards a delay starting around 2070. In the SSP585 scenario, the average SOS advanced gradually at a rate of approximately 0.14 days per year. Moreover, the standard deviation of the simulated SOS by the 13 spring phenology models exhibited a significant increase at a rate of 0.04 days per year. On average, two-phase models exhibited larger standard deviations than one-phase models after approximately 2050. Models driven solely by temperature showed larger standard deviations after 2060 compared to models driven by both temperature and photoperiod. Our findings suggest investigating the release mechanisms of endodormancy phase and incorporating new insights into future phenological models to better simulate the changes in plant phenology. [ABSTRACT FROM AUTHOR]

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between <0.01 Tg C/a and 0.20 Tg C/a in various districts and counties. Over time, forest growth gradually slowed, and carbon sequestration rates also decreased. Under the four future climate scenarios, the SSP5-8.5 pathway had the highest carbon sequestration rate. Natural factors had the greatest influence on changes in carbon sequestration rate. This result provides data support and scientific reference for the planning and control of forests and the enhancement of carbon sequestration capacity in Chongqing. [ABSTRACT FROM AUTHOR]

Due to the increase in greenhouse gases, water and climate crises, increasing population, and decreasing water resources, accurately predicting the changes in the GWL is essential for the management of water resources. For this purpose, in this research, the MIROCES2L model was used to predict the climatic parameters of Birjand Plain under three scenarios of the sixth climate change report: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The minimum temperature, maximum temperature, and precipitation parameters from these three scenarios were measured using the CMhyd model. The results indicated that the minimum and maximum temperature would generally increase in the future under the influence of climate change, but precipitation has a sinusoidal behavior and has a decreasing trend in the summer and spring seasons and an increasing trend in the winter and autumn seasons. Then, three ANN, NIO, and MLR models were employed to simulate groundwater depletion. The results indicated that the evaluation of the performance criteria of the NIO model is superior to the other two models, and it was chosen as the model for predicting groundwater depletion in the future period under the influence of climate change based on all three mentioned scenarios. The final results of this research indicated that the GWL of Birjand Plain in the future period (2024–2041) under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios would respectively decrease to 5.58m, 5.13m, and 5.38. The results of this research indicate that the need for sustainable management to conserve groundwater resources is also very important in the study area. [ABSTRACT FROM AUTHOR]

The distribution of species is mostly influenced by climate synergistic effects and land use. The prediction of endangered species is dependent on fine-scale environmental features, especially in forests. The capture of fine-scale suitable habitats is bounded by low spatial resolution and coarse categorization. In this study, we improved the land use information and forest spatial detail through high forest thematic resolution land use data. To understand the relative influence of environmental factors, suitable habitat models for Chinese endangered tree species (Firmiana danxiaensis) under three climate and high forest thematic resolution land use conditions was constructed. We also assess F. danxiaensis 's response to climate and land use, and differential performance in land use on the suitable habitat. The area of suitable habitat for F. danxiaensis grows slowly under the SSP1-2.6 (shared social pathway scenario) scenario, decreases by 21.33% from the present to 2090 under the SSP2-4.5 scenario, and expands significantly under the SSP5-8.5 scenario. We conclude that high forest thematic resolution land use is beneficial in capturing species' requirements for specific habitats and especially necessary in predicting endangered species. At the same time, the distribution of suitable habitats for species is primarily driven by climate, and limited their development by land use. The study shows that the synthetic effects of climate and land use change on F. danxiaensis are positive, with a marked trend toward the northeast. As a consequence, some endangered species are able to benefit from future SSPs scenarios, in particular they respond more strongly to the SSP5-8.5 scenario than to the other scenarios. Assessing the role of changes in the climate and high forest thematic resolution land use on F. danxiaensis will help promote sustainable land use management and contribute to the conservation policy for Chinese endangered species.

Floods are one of the extreme events and widespread natural disasters that significantly affect the civil infrastructure and livelihoods of people. Recently, climate change has significantly altered the rainfall pattern and increased flood events worldwide, especially in India. Therefore, it has become essential to map potential flood inundation regions for various future extreme events to develop appropriate flood mitigation and management strategies. This study aims to develop flood inundation maps for different return periods under climate change scenarios for the Chaliyar basin, Kerala. The Hydrologic Engineering Center-Hydrologic Modelling System model was used to simulate streamflow under SSP2-4.5 and SSP5-8.5 scenarios. Later, flood inundation maps were developed for different return periods using the Hydrologic Engineering Center-River Analysis System model. It was observed that for the near future (2031–2040) and far future (2071–2080), simulated streamflow is higher for SSP5-8.5. However, the mid-future (2051–2060) resulted in a higher streamflow for SSP2-4.5 than the SSP5-8.5 scenario. A maximum of 19.52 m of water surface elevation occurred at Kizhupparamba during mid-future for SSP2-4.5, followed by 18.38 m of water surface elevation at Cheekode during the near future for SSP5-8.5, for 100-year return period events. This study showed that hydrologic and hydraulic models could be effectively combined for mapping the flood inundation areas. HIGHLIGHTS This study integrates the hydrological model, hydraulic model and climate change scenarios to develop flood inundation maps for various return periods.; SSP2-4.5 and SSP5-8.5 are considered for climate change scenarios to simulate the streamflow for the near future (2031–2040), mid-future (2051–2060) and far future (2071–2080).; Future streamflow in the Chaliyar basin will likely to increase for both SSP2-4.5 and SSP5-8.5 scenarios.;

Wave energy is still insufficiently explored and exploited as a future energy source. Climate change is an additional force that affects energy potential changes. Therefore, this study aims to evaluate the wave energy under climate change and to project it for the near (2025–2044) and far (2081–2100) future by applying the wave energy flux (WEF) approach and statistical relations between wind speeds and wave heights. The study was concentrated on the Baltic Sea nearshore at the Lithuanian territorial water. The analysis of existing relations between wind speeds and wave heights was found based on historical observations of the reference period (1995–2014), and the projections of WEF were created using the downscaled output of best-fit global climate models (GCMs) according to four scenarios of Shared Socioeconomic Pathways (SSP). The results indicated strong relations between wind speed and wave height, especially for the west-origin winds. Depending on the selected scenarios, the projected WEF may increase up to 10% (SSP5-8.5) and 11% (SSP1-2.6) in the near and far future respectively. The absence of large differences between the periods may be caused by the rough resolution of grid cells of GCMs. The comparison with the results based on regional climate models output could be a future perspective in order to reach a better representation of regional forces and to introduce more clarity to the obtained results. The results of this study may be advantageous for the primary planning of renewable energy sources (RES) development, especially in the face of climate change.

Future climate change and its impact on drought is critical for Uzbekistan, located in Central Asia, the world’s largest arid zone. This study examines the evolving intensity of climate change and drought events using multi-model ensembles (MMEs) derived from the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) simulated under the Representative Concentration Pathway and Shared Socioeconomic Pathway (RCP and SSP) scenarios. The projections show different rates of increase in temperature and precipitation under the RCPs and SSPs. Projected temperature increases are expected to reach up to 2–2.5 °C under SSP1-2.6, SSP2-4.5, and SSP3-7.0, by mid-century. By 2080–2099, an increase is projected of 2–3 °C in monthly mean temperatures throughout the year (SSP1-2.6), and a more pronounced increase in summer up to 3–4 °C (SSP2-4.5) and 4–6 °C (SSP3-7.0), with a marked contrast in conditions between the mountainous and desert regions of Uzbekistan. Regional changes in precipitation over the study periods show relatively little variability, except for FD, where notable trends are found. Under SSP1-2.6 and SSP2-4.5, the increase in precipitation is relatively modest, whereas the changes in SSP3-7.0 are more substantial, with some regions experiencing variations of up to 10–20 mm per period. The Standardized Precipitation Evapotranspiration Index (SPEI), calculated based on the projected temperature and precipitation, provides an estimate of future drought trends. Our results show increasing aridity under all scenarios by mid-century, with longer-term projections indicating stabilization around different SPEI values by 2100: RCP2.6 and SSP1-1.9 stabilize around −1.0; RCP4.5, RCP6.0, SSP2-4.5, and SSP3-7.0 stabilize around −1.5; while RCP8.5 and SSP5-8.5 scenarios project values of −2 or less by 2100. Notable differences in the SPEI index are found between lowland and foothill regions. In view of Uzbekistan’s heavy reliance on agriculture and irrigation, which are the sectors that are expected to be mostly affected by climate change, our study provides a scientific basis for informed policy decision-making. This includes various aspects such as planning and management water resources, as well as the broader socioeconomic development of the country.

This study identifies the impact of climate change on precipitation in watersheds of Tehran water supply in the horizon 2025-2050 under the scenarios of CMIP6. Therefore, first, the changes in precipitation trends in the base period were calculated using the precipitation data of the study area's 33 synoptic and rain gauge stations for the period 1989-2019. Then changes soon based on four models, CanESM5, CNRM-CM6-1, MIROC6, and MRI-ESM2-0, and under two scenarios, SSP2-4.5 and SSP5-8.5 were projected. Due to the large scale of atmospheric general circulation models, two methods of linear scaling (LS) and distribution mapping (DM) were used for downscaling selected GCMs. Finally, the DM method was chosen to produce climatic scenarios due to its higher accuracy after calculating the validation indices of the models. The trend tests showed that in a significant part of the study basin, an increasing trend (with confidence levels of 0.95 and 0.99) in autumn and a decreasing trend in winter are observed during the observation period. In spring, the eastern and northeastern regions show a decreasing trend, and the northern and northwestern regions show an increasing precipitation trend. According to the output of GCM models, spring precipitation under the SSP2-4.5 scenario increases in all stations and decreases in 17 stations according to SSP5-8.5. Changes in summer precipitation are not significant in the present and future conditions, and winter and autumn are somewhat consistent with the changes in the observation period. Thus, in winter, precipitation, according to both scenarios, is less than in the current situation and is more in autumn under the SSP5-8.5 scenario. The effect of climate change on the amount of water in the watersheds also showed that the maximum water volume in the current conditions is related to the Karaj watershed. Between 2025 and 2050, the water content of this watershed increases by 8.9% in the CNRM-CM6-1 model according to the SSP2-4.5 But in MIROC6, MRI-ESM2-0, and CanESM5 models, it decreases by 5.3, 6.3, and 59.6 percent, respectively.

This study examines future changes in extreme heat stress over East Asia and its sub-regions using wet bulb globe temperature (WBGT) based on the CORDEX East Asia Phase II multiple Regional Climate Model (RCM) simulations performed under Shared Socioeconomic Pathways (SSP) scenarios. Daily maximum WBGTs (WX) are obtained from 3-hourly bias-corrected WBGTs and their future changes in the late 21st century (2081–2100) are analyzed with respect to the current period (1979–2014). Summer mean WX is projected to increase by 3.2 °C (SSP1-2.6: low emission) to 7.6 °C (SSP5-8.5: high emission) over East Asia, dominated by temperature increases. Relative humidity decreases over many regions, slightly offsetting WX increases (up to −6%), while it increases in northeastern and northern China, intensifying WX increases (up to +14%). This humidity-induced WX increase becomes stronger during hottest WX days (summer top 5%) and also under low emission scenarios (up to +33%). For sub-regional projections, extreme heat stress day (EHD) is defined when WX exceeds its 95th percentile in at least 10% of the area. RCMs project on average a 10 times increase of EHD frequency under the SSP5-8.5 scenario. The EHD magnitude, which combines the intensity and area extent of EHD events, is also expected to increase dramatically throughout East Asia, reaching a range of 3.2–3.5 °C∙fraction compared to the current (0.1–0.2 °C∙fraction). Further, EHDs are projected to start earlier and end later, lasting much longer (85–140 days) than the current condition (5–6 days) in the SSP5-8.5 scenario. RCMs exhibit a good agreement in WX and EHD projections with some noticeable differences in in-land sub-regions. Our results indicate that severe heat stress will affect the whole East Asia throughout and beyond the summer season and, in particular, southern sub-regions will be affected by more-intense and longer-lasting extreme heat stress events.

At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties. Plain Language Summary: As climate change continues, extreme rainfall is expected to become more frequent and intense. This can lead to changes in landslide risks and how population is exposed to them. While previous studies have mainly focused on how the landslide likelihood may change in time, few have paid attention to how dynamic socioeconomic factors play a role in determining landslide risk. In this study, we used multiple global climate models and different population data sets to investigate how different sources of population information can affect estimates of future landslide risks in China. Overall, we found that landslide hazard is likely to increase in the future. However, when we incorporated different population data into our models, we found divergent results in terms of the number of people exposed to these phenomena. Depending on the population data used, the estimates ranged from a decrease of about 64% to an increase of 48%. These different outcomes were mainly due to variations in population data we used, although climate models also had an impact. These conflicting estimates highlight the need to consider changes in elements at risk when assessing disaster risks and developing targeted measures to adapt to and mitigate the effects of climate change. Key Points: Future landslide exposure is assessed by combining landslide model with spatially explicit population and global climate model dataPotential impact areas and frequency of landslides in China under climate change are projected to increase by 0.4%–2.7% and 4.7%–20.1%, respectivelyVarying sourced population projections may induce divergent signals (−64% to +48%) of landslide‐exposed population under climate change [ABSTRACT FROM AUTHOR]

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China’s largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people’s daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase.

Abstract At first glance, assessing future landslide‐exposed population appears to be a straightforward task if landslide hazard estimates, climate change, and population projections are available. However, the intersection of landslide hazard with socioeconomic elements may result in significant variation of estimated landslide exposure due to considerable variations in population projections. This study aims to investigate the effects of different sources of population data on the evaluation of landslide‐exposed population in China under four Shared Socioeconomic Pathways (SSPs) scenarios. We utilize multiple global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 and six high‐resolution spatially explicit static and dynamic population data sets to drive available landslide models. The results indicate an overall rise in landslide hazard projections, with an increase in the potential impact area of 0.4%–2.7% and an increase in the landslide frequency of 4.7%–20.1%, depending on the SSPs scenarios and future periods. However, the likely changes in future landslide exposed population, as modeled by incorporating population data from different sources with landslide hazard, yield divergent outcomes depending on the population data source. Thus, some of the projections depict an increase in future landslide exposure, while others show a clear decrease. The nationwide divergence ranged from −64% to +48%. These divergent findings were mainly attributed to differences in population data and a lesser extent to variations in GCMs. The present findings highlight the need to pay closer attention to the dynamic evolution of the elements at risk and the associated data uncertainties.

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai'an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the performance of six models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in reproducing the monthly cycle of precipitation and maximum surface air temperature of the Hodna region in central Algeria during the baseline period 1901–2014. The six climate models selected are as follows: ACCESS-ESM4, BCC-CSM2-MR, CanESM5, GFDL-ESM1-5, IPSL-EM6A-LR, and MIROC6. The best-performing models are determined by comparing observation (CRU-TS) and historical data. The evaluation procedure is performed using the compromise programming method and multiple statistical metrics including mean bias error (MB), root mean square error (RMSE), and pattern determination coefficient (R2). The first two best-performing models are used for precipitation (IPSL-EM6A-LR) and maximum surface air temperature (GFDL-ESM4). Four future periods are considered: 2021–2040, 2041–2060, 2061–2080, and 2081–2100 for SSP1-2.6 and SSP3-7.0. For the most optimist scenario, the maximum surface air temperature may increase in the next decades in comparison with observed and simulated values by 2 °C, but then almost stabilizes and reaches an average monthly increase of 28.53 °C. The same is shown in the precipitation projection of 48.75 mm/month by 2100. Eventually, the SSP1-2.6 scenario does not show any current signs in the current Hodna basin climate on a global scale. Future precipitation projections under Hodna's SSP3-7.0 may decrease with an average value of 10 mm in a monthly scale. This decrease becomes mostly in the center and southeastern parts. Maximum surface air temperature can rise steadily from 28 to 31 °C and reach its maximum of 4.5 °C by 2100. The outcomes of this study will evaluate how projected changes will affect droughts and floods. [ABSTRACT FROM AUTHOR]

Multimodel ensemble (MME) approach would help modellers to know the advantages of individual global circulation models (GCMs) and to avoid the weaknesses associated with them, and it would help the river basin modellers to make appropriate modelling decisions. The study highlights the river basin‐scale development of MME as a convenient way to reduce the parameter and structural uncertainties in the Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs simulations after identifying the best five CMIP6 GCMs based on the rating metric calculations. Furthermore, the performance of the MME was enhanced by integrating three machine learning algorithms (artificial neural network [ANN], random forest [RF], support vector machine [SVM]). Subsequently, comparative assessment depicted the improved performance in MME‐integrated ML algorithms compared to simple arithmetic mean (SAM) in simulating observed precipitation (P), maximum temperature (Tmax), and minimum temperature (Tmin) over the Damodar River basin (DRB), India. The statistical metrics indicate that the SVM and RF methods yielded better results than SAM and ANN methods, thus selected for future projections. The robustness of the MME‐RF and MME‐SVM approach has also been observed while capturing the spatial pattern as IMD‐observed with well representation of climate indices for both wet and dry seasons. Future projections with MME‐SVM and MME‐RF suggested a possible rise in mean annual P in the range of 1.4–15% and 6.8–39% with an increasing trend in temperature (Tmax, Tmin) under the SSP245 and SSP585 scenarios, respectively. Replicating the spatial pattern of the future climatic variables projections evinced a warmer and drier climate in the southwest part of the DRB for both SSP scenarios during wet and dry season and thence warned a probable drier condition on the southwest part of the DRB in future time slices. [ABSTRACT FROM AUTHOR]

The Mediterranean is recognized as one of the most sensitive regions regarding climate change. The northern sector winds are a dominant feature of summer low-tropospheric circulation over the Aegean basin in the eastern Mediterranean (EMed). This study is an updated assessment that uses state-of-the-art tools in order to investigate the projected changes in the meridional wind speed and Etesian regime during summer period (June-July-August) over the 21st century. The analysis is based on 17 Global Climate Models simulations (GCMs) available from Coupled Model Intercomparison Project Phase 6 (CMIP6) covering the historical period (from 1971 to 2014) and the future period (from 2015 to 2100) under two Shared Socioeconomic Pathways (SSPs), an intermediate-, and a very high-emission scenario (SSP2-4.5 and SSP5-8.5). Additionally, the results from GCMs analysis are compared to ERA5 reanalysis for the historical period from 1971 to 2000. Our findings suggest that the majority of GCMs reproduce the spatial pattern of Etesians but underestimate the meridional wind speed by about 0.5 to 1.0 m/s, as compared to ERA5. During the future period, the meridional wind speed is projected to be increased over the Aegean basin, mainly during the last period of 21st century. Findings show that the majority of GCM simulations (12 out of 17) show an increase in meridional wind speed of about 0.2 to 1.4 m/s for SSP5-8.5 and 0.2 to 0.6 m/s for SSP2-4.5, as compared to historical period from 1971 to 2000.

Due to a series of human activities like deforestation and land degradation, the concentration of greenhouse gases has risen significantly. Forest vegetation is an important part of forest ecosystems with high carbon sequestration potential. Estimates of the carbon sequestration rate of forest vegetation in various provinces and districts are helpful to the regional and global Carbon cycle. How to build an effective carbon sequestration potential model and reveal the spatiotemporal evolution trend and driving factors of carbon sequestration potential is an urgent challenge to be solved in carbon cycle simulation and prediction research. This study characterized the carbon sequestration status of forest vegetation using the modified CASA (Carnegie-Ames Stanford Approach) model and estimated the carbon sequestration potential from 2010 to 2060 using the FCS (Forest Carbon Sequestration) model combined with forest age and biomass under the four future Shared Socioeconomic Pathways (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, then proposes natural, social, and economic perspectives. This study found that the average NPP of the forest vegetation in Chongqing from 2000 to 2020 was 797.95 g C/m2, and the carbon storage by 2060 was 269.94 Tg C. The carbon sequestration rate varied between

In the background of global climate change and rapid urbanization, extreme climate events are frequent, and highly urbanized areas flooding problems are becoming increasingly prominent. It becomes important to develop qualitative scenario storylines to assess future flooding risk in a changing environment over the highly urbanized areas. In this study, the future (2030–2050) flood risk in the Pearl River Delta (PRD) region of China was assessed based on the MaxEnt model. We have developed four future scenarios under different emission conditions based on the representative concentration pathways (RCPs) and the shared socio-economic pathways (SSPs). The MaxEnt model was trained by using flood hazard sample point data and flood risk indicators, meanwhile, food risk prediction with high accuracy was obtained. We analyzed the influencing factors of flood risk and predicted the flood risk of the PRD under four future scenarios. According to the results, there is a significant increase in the size and proportion of high flood risk areas in most scenarios. Flood risk under scenario SSP5-RCP8.5 are expected to be the most serious in the future. Population density (POPD) and Gross domestic product density (GDPD) have the highest explanatory power for flood risk. This study predict the changes of flood risk under the combined influence of climate change and human activities, and hoped to provide a reference for future planning and disaster mitigation construction in PRD cities.

Projecting future air pollution and related health burdens remains challenging because of the complex interactions among future emissions, population, and climate change. In this study, we estimated the premature deaths attributed to ambient fine particulate matter (PM2.5) and ozone (O3) from 2015 to 2100 under four socioeconomic climate scenarios based on an age-stratified assessment method. We found that PM2.5 will decrease in all shared socioeconomic pathway (SSP) scenarios and O3 will decrease in the SSP1-2.6 and SSP2-4.5 scenarios, contributing to a decrease in premature mortality together with the declining total population in China. However, the benefits of a decline in population size and PM2.5 and O3 concentrations over time will be largely offset by population aging, and premature death caused by PM2.5 and O3 will continue to rise till 2060–2080. This impact was greater for the O3-related deaths than those for PM2.5. Our study highlights the importance of future prevention strategies that must jointly improve air quality and susceptibility to aging.

The net effective temperature (NET), an index that includes the combined effects of temperature, humidity, and wind, was used along with temperature to assess the impacts of climate change on the heat stress perception in East Asia, one of the regions considered most vulnerable to heat stress. The need for dynamic downscaling has been emphasized because the regional effects of climate change do not follow the global levels linearly. In this study, daily maximums calculated from the 3‐hourly data downscaled by five different regional climate models from four coupled general circulation models participating in Coordinated Regional Climate Downscaling Experiment‐East Asia phase 2 were utilized. To account for the fact human beings acclimate to their environments, 95th percentile of the maximum temperature and maximum NET was used along with the average boreal summer maximum temperatures/NETs. The performance of the models was assessed first, which showed that the models reproduced the current climate well. Future projections revealed an increase in both average and 95th percentile of the maximum temperature and NET over the entire domain for both the RCP8.5 and SSP5‐8.5 scenarios. The increase in heat stress (NET) was slightly larger than the temperature itself, with an increase of up to 7/10°C for temperature and 8/11°C for NET in RCP8.5/SSP5‐8.5, respectively. The overall increases in temperature and NET were projected to be higher in the higher latitudes, while the increase in the frequency of the temperature and NET extremes was predicted to be higher in the already vulnerable regions in the southern part of the domain. [ABSTRACT FROM AUTHOR]

Based on the 1-km resolution long time series temperature data set, the spatial and temporal patterns of the magnitude and trend of the four-season mean temperature changes in the historical period (2001-2020), and in the future period (2021-2100) under the low forcing scenario (SSP119), medium forcing scenario (SSP245) and high forcing scenario (SSP585) are analyzed using the distance level method, Mann-Kendall trend test method and Sen's slope estimation method, with a view to providing a basis for developing detailed regional adaptation strategies in the context of climate warming. The results show that: (1) compared with the historical period, the average temperature of the four seasons in the future will increase in general under the three scenarios, and the area with the largest increase in summer is 66.70% under SSP119, 37.37% and 99.06% under SSP245 and SSP585, respectively. At the same time, the overall variation range of seasonal mean temperature under the three SSP scenarios is significantly different, which is moderated under SSP119, followed by SSP245, and the largest increase range under SSP585. (2) In the historical period, compared with other seasons, the significant rise in mean temperature in spring had the fastest rate (0.68±0.24°C⋅10y-1) and the largest area share (14.44%), mainly distributed in North China, Yunnan, Guizhou, Jiangsu and Zhejiang provinces. (3) In the future period, the regional seasonal mean temperature in China shows an overall rising trend with significant spatial differences; among them, under the SSP119 scenario, the regions with significantly rising mean temperature in spring and winter are mainly concentrated in southern China and the local area of the Qinghai-Tibet Plateau, accounting for 29.03% and 25.58% of the area, respectively; under the SSP245 and SSP585 scenarios, the seasonal mean temperature in all regions of China shows a significant upward trend; under the SSP585 scenario, the seasonal mean temperature in the north increases at a faster rate than that in the south, and the national region has the fastest significant rate of increase in winter (0.66±0.09°C⋅10y-1). [ABSTRACT FROM AUTHOR]

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai’an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment.

This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions.

This study presents an analysis of the changes in extreme temperatures over major river basins in China under different shared socioeconomic pathway (SSP) scenarios based on a set of dynamical downscaling over CORDEX East Asia, performed with the regional climate model RegCM4 at a resolution of 25 km, driven by the global climate model FGOALS‐g3. The results indicated that the dynamical downscaling tended to reduce the warm (cold) biases of the present‐day daily maximum (TXx) and minimum temperatures (TNn) simulated by FGOALS‐g3. Under the SSP scenarios, substantial increases in TXx and TNn and the number of warm days and warm nights throughout China were projected by both models. The average increase in TXx in most of the river basins ranged from 1 to 2°C under the SSP1‐2.6 scenario, from 2 to 3°C under the SSP2‐4.5 scenario, and from 3 to 5°C under the SSP5‐8.5 scenario. Reductions in the number of cold days and cold nights were projected across China, but these reductions were smaller than the increases in the number of warm days and warm nights. The interregional variability in the increases in TXx and TNn was more pronounced in the SSP5‐8.5 scenario and the RegCM4 model than in the other SSP scenarios and the FGOALS‐g3 model, respectively. Larger percentages of the land area and population of China would be affected by greater increases in extreme high temperatures and TNn. Under the SSP2‐4.5 (SSP1‐2.6) scenario, 20–65% (50–95%) of the impacts of extreme temperatures under the SSP5‐8.5 scenario could be avoided across most regions in China. The avoided impacts of warm days and warm nights were greater than those of the other extreme temperature indices. More of the impacts of extreme temperatures could be avoided in the Huai River basin, Yellow River basin, Hai River basin, and Northwest Interior River basin than in the other river basins in China. [ABSTRACT FROM AUTHOR]

Drought is a meteorological disaster that has serious economic and environmental impacts. Therefore, for drought assessment, it is important to not only analyze the current state of drought using observed data but also future droughts by considering climate change. Although shared socioeconomic pathways (SSPs) have been developed, studies regarding their application in drought analysis in Korea are insufficient. In this study, the standardized precipitation index (SPI) and reconnaissance drought index (RDI) for future droughts in Korea were calculated and analyzed using climate data based on SSP scenarios. The precipitation and temperature data from SSP1-2.6 and SSP5-8.5 scenarios were analyzed. In both scenarios, temperature and precipitation increased toward the long-term future, with the increase being more in the SSP5-8.5 scenario than in the SSP1-2.6 scenario. Using these data, the number of drought occurrences was found to be smaller, and the two drought indices were found to be lower in SSP5-8.5 scenario than in the SSP1-2.6 scenario, implying that more severe droughts may occur in the former than in the latter. In addition, in SSP1-2.6 scenarios, both SPI and RDI increased toward the long-term future, while in SSP5-8.5 scenario, SPI increased, and RDI decreased toward the long-term future. [ABSTRACT FROM AUTHOR]

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very important for the African continent. This study investigates the future changes in drought events based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model in the Coupled Model Intercomparison Project phase six (CMIP6) datasets for four shared socio-economic pathways (SSP): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5; and three time slices: near future (2020–2039), mid-century (2050–2069), and end-of-century (2080–2099), relative to a historical baseline period (1995–2014). The interannual variability and trends of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith methods for measuring potential evapotranspiration (PET) are used to estimate future droughts. The temporal analysis shows that the drought frequency, intensity, and affected area will increase throughout the 21st century. Among the scenarios, SSP3-7.0 and SSP5-8.5 project a larger upward trend in drought characteristics than SSP1-2.6 and SSP2-4.5. The spatial pattern shows drought frequency decreases in humid regions and increases in non-humid regions across Africa. For all SSP scenarios, the projected wetting trend per decade ranges from 0.05 to 0.25, while the drying trend per decade ranges from −0.05 to 0.25. A regional trend analysis revealed key differences in spatial pattern, with varied trend projections of wetter and drier conditions in humid and non-humid regions under all SSP scenarios. Drier conditions are expected to intensify in Southern Africa under all SSP scenarios but are projected to be more intense under either SSP3-7.0 and SSP5-8.5. In general, the projected wetter trends in humid areas may favor agricultural production and ecological conservation, and drier trends in non-humid regions may call for the possible adoption of tailor-made drought adaptation strategies and development programmes to minimize impacts. [ABSTRACT FROM AUTHOR]

To connect a wide range of research communities including climate change mitigation and adaptation activities, the Intergovernmental Panel on Climate Change (IPCC) 6th assessment report (AR6) employs the Shared Socioeconomic Pathway (SSP) scenarios. The CORDEX East Asia calls for a dynamical downscaling of climate change projections under the new SSP scenarios. In this study, the state-of-the-art regional climate model RegCM4 with a horizontal resolution of 25 km was used to dynamically downscale future climate changes, which were derived from the global climate model FGOALS-g3, under multiple SSP scenarios over the CORDEX East Asia Phase II domain. Compared to the driving global model FGOALS-g3, the downscaling exhibited added value in the simulation of present-day mean and extreme rainfall over China in terms of both spatial pattern correlation and biases. The largest improvements were found over the Pearl River Basin and the Yangtze River Basin. For the period of 2081–2100 under the SSP2-4.5 scenario, the areas in China with increases in the projected mean and extreme rainfall generally overlapped with the areas with substantial climatological mean and extreme rainfall in both RegCM4 and FGOALS-g3, which highlights the importance of simulated climatology in projecting future rainfall changes. The moisture budget analyses indicated that the projected increases in summer mean rainfall were mainly attributed to thermodynamic processes associated with the increased moisture. The mean and extreme rainfall was most sensitive to the increased emission scenarios in the Northwest Interior River Basin and least sensitive in southern China. From SSP1-2.6 to SSP5-8.5, larger fractions of land area and population would be affected by greater increased and more intense rainfall. If the emissions were reduced from SSP5-8.5 to SSP1-2.6, over 80% of the impacts would be avoided over most regions in China. [ABSTRACT FROM AUTHOR]

Global warming has caused widespread permafrost degradation, but the geographic regularity of permafrost degradation is unknown. Here, we investigated the three-dimensional features of future permafrost degradation on the Qinghai-Tibetan Plateau. Our findings show that permafrost degradation under shared socioeconomic pathways (SSPs) has obvious three-dimensional characteristics. In comparison to latitude and aridity, permafrost degradation is closely related to elevation, i.e. it slows with elevation, a phenomenon known as elevation-dependent degradation. The pattern of elevation-dependent degradation is consistent across four subzones and is strongly linked to thermal conditions that vary with elevation. Under SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, remarkable elevation-dependent warming (EDW) is observed at 3600–4900 m, but changes in mean annual ground temperature of permafrost and EDW as altitude rises are anti-phase. Under any SSP, the magnitude of mean annual air temperature along altitude belts determines the degree of permafrost degradation ( R ^2 > 0.90). This research provides new insight on the evolution of permafrost.

Peatlands only cover a small fraction of the global land surface (∼3%) but store large amounts of carbon (∼600 GtC). Drainage of peatlands for agriculture results in the decomposition of organic matter, leading to greenhouse gas (GHG) emissions. As a result, degraded peatlands are currently responsible for 2%–3% of global anthropogenic emissions. Preventing further degradation of peatlands and restoration (i.e. rewetting) are therefore important for climate change mitigation. In this study, we show that land-use change in three SSP scenarios with optimistic, recent trends, and pessimistic assumptions leads to peatland degradation between 2020 and 2100 ranging from −7 to +10 Mha (−23% to +32%), and a continuation or even an increase in annual GHG emissions (−0.1 to +0.4 GtCO _2 -eq yr ^−1 ). In default mitigation scenarios without a specific focus on peatlands, peatland degradation is reduced due to synergies with forest protection and afforestation policies. However, this still leaves large amounts of GHG emissions from degraded peatlands unabated, causing cumulative CO _2 emissions from 2020 to 2100 in an SSP2-1.5 °C scenario of 73 GtCO _2 . In a mitigation scenario with dedicated peatland restoration policy, GHG emissions from degraded peatlands can be reduced to nearly zero without major effects on projected land-use dynamics. This underlines the opportunity of peatland protection and restoration for climate change mitigation and the need to synergistically combine different land-based mitigation measures. Peatland location and extent estimates vary widely in the literature; a sensitivity analysis implementing various spatial estimates shows that especially in tropical regions degraded peatland area and peatland emissions are highly uncertain. The required protection and mitigation efforts are geographically unequally distributed, with large concentrations of peatlands in Russia, Europe, North America and Indonesia (33% of emission reductions are located in Indonesia). This indicates an important role for only a few countries that have the opportunity to protect and restore peatlands with global benefits for climate change mitigation.

Forecasting alterations in ambient air pollution and the consequent health implications is crucial for safeguarding public health, advancing environmental sustainability, informing economic decision making, and promoting appropriate policy and regulatory action. However, predicting such changes poses a substantial challenge, requiring accurate data, sophisticated modeling methodologies, and a meticulous evaluation of multiple drivers. In this study, we calculate premature deaths due to ambient fine particulate matter (PM 2.5 ) exposure in India from the 2020s (2016-2020) to the 2100s (2095-2100) under four different socioeconomic and climate scenarios (SSPs) based on four CMIP6 models. PM 2.5 concentrations decreased in all SSP scenarios except for SSP3-7.0, with the lowest concentration observed in SSP1-2.6. The results indicate an upward trend in the five-year average number of deaths across all scenarios, ranging from 1.01 million in the 2020s to 4.12-5.44 million in the 2100s. Further analysis revealed that the benefits of reducing PM 2.5 concentrations under all scenarios are largely mitigated by population aging and growth. These findings underscore the importance of proactive measures and an integrated approach in India to improve atmospheric quality and reduce vulnerability to aging under changing climate conditions.

• We firstly applied the high forest thematic resolution land use to assess species distribution; • Firmiana danxiaensis was selected, as it is a rare tree species that is endemic to China; • Both future climate and land use change data are projections for the SSPs scenarios. The distribution of species is mostly influenced by climate synergistic effects and land use. The prediction of endangered species is dependent on fine-scale environmental features, especially in forests. The capture of fine-scale suitable habitats is bounded by low spatial resolution and coarse categorization. In this study, we improved the land use information and forest spatial detail through high forest thematic resolution land use data. To understand the relative influence of environmental factors, suitable habitat models for Chinese endangered tree species (Firmiana danxiaensis) under three climate and high forest thematic resolution land use conditions was constructed. We also assess F. danxiaensis 's response to climate and land use, and differential performance in land use on the suitable habitat. The area of suitable habitat for F. danxiaensis grows slowly under the SSP1-2.6 (shared social pathway scenario) scenario, decreases by 21.33% from the present to 2090 under the SSP2-4.5 scenario, and expands significantly under the SSP5-8.5 scenario. We conclude that high forest thematic resolution land use is beneficial in capturing species' requirements for specific habitats and especially necessary in predicting endangered species. At the same time, the distribution of suitable habitats for species is primarily driven by climate, and limited their development by land use. The study shows that the synthetic effects of climate and land use change on F. danxiaensis are positive, with a marked trend toward the northeast. As a consequence, some endangered species are able to benefit from future SSPs scenarios, in particular they respond more strongly to the SSP5-8.5 scenario than to the other scenarios. Assessing the role of changes in the climate and high forest thematic resolution land use on F. danxiaensis will help promote sustainable land use management and contribute to the conservation policy for Chinese endangered species. [ABSTRACT FROM AUTHOR]

Climate change is a major challenge for human society and the natural environment. Evidence suggests that human activities play a role in increasing temperature at various temporal-spatial scales. The effects of climate change can be assessed by analyzing air temperature trends. According to the latest IPCC report, global average temperatures will increase by 1.5 degrees Celsius by the end of this century. The main purpose of this study is to assess CMIP6 projected temperature over different climate zones of Iran and its trend in the future. The result of this study can be useful for a wide range of management areas, especially the study of water resources, snow reserves, agriculture, and tourism. In this study, the mean annual temperature data of 43 synoptic stations of the Iran Meteorological Organization (IRIMO) were obtained from 1990 to 2009. To evaluate the anomaly and temperature trend in Iran until the end of the 21st century, the data of three models BCC-CSM2-MR, CAMS-CSM1-0, and MRI-ESM2-0 of CMIP6 models under two scenarios of SSP2.4-5 and SSP5.8-5 were used. We divided the period into four twenty-year periods, which are the first period (2020-2040), the second period (2041-2061), the third period (2061-2080), and the fourth period (2081-2100), respectively. To evaluate the air temperature which is the output of selected CMIP6 models, three statistical measures of RMSE, NSE and KGE were used. Using the Delta Change Factor (DCF) method, the bias of the models was corrected. Then, non-parametric Man-Kendall (MK) and Sen's Slope Estimator tests were used to analyze trend analysis and trend slope in long-term data series. The maximum temperature is seen on the northern coast of the Persian Gulf and the Sea of Oman and the minimum temperature is seen in the northwest following the heights of the Zagros and also in the north of Iran (Alborz Mountains). The minimum annual temperature of 10.80°C was calculated based on observed data for the period 1990-2009, and the maximum temperature was 27.90°C on the coasts of the Oman Sea in southeastern Iran and Khuzestan province on the shores of the Persian Gulf in southwestern Iran. The intensity of the increase in temperature in Iran in mountainous areas is mainly due to the increase in the minimum temperature rather than to the maximum one. Generally, the intensity of the warming in Iran is mostly projected in cold and temperate regions. There is also a tendency for the temperature to rise further at higher latitudes. The projected temperature of CMIP6 models based on the SSP2.4-5 and SSP8.5-8 scenarios in Iran over four 20-year periods from 2020 to 2100 showed that the average slope of the temperature trend in Iran will reach 0.05°C per year, which shows an increase by a factor of 0.01 throughout Iran. As a general result, the annual trend of air temperature in Iran, based on observational data and projected output of CMIP6 models, shows warmer climate conditions for Iran. Temperature anomaly was not negative in any of the scenarios and periods; whereas positive anomaly is seen throughout the country. This increase in anomaly can be a major threat to the country's water resources. [ABSTRACT FROM AUTHOR]

In a warming climate, drought events are projected to increase in many regions across the world, which would have detrimental impacts on water resources for agriculture activity and human life. Thus, projecting drought changes, especially the frequency of future drought events, is very important for the African continent. This study investigates the future changes in drought events based on the France Centre National de Recherches Météorologiques (CNRM-CM6) model in the Coupled Model Intercomparison Project phase six (CMIP6) datasets for four shared socio-economic pathways (SSP): SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5; and three time slices: near future (2020–2039), mid-century (2050–2069), and end-of-century (2080–2099), relative to a historical baseline period (1995–2014). The interannual variability and trends of the self-calibrating Palmer Drought Severity Index (scPDSI) based on the Penman–Monteith methods for measuring potential evapotranspiration (PET) are used to estimate future droughts. The temporal analysis shows that the drought frequency, intensity, and affected area will increase throughout the 21st century. Among the scenarios, SSP3-7.0 and SSP5-8.5 project a larger upward trend in drought characteristics than SSP1-2.6 and SSP2-4.5. The spatial pattern shows drought frequency decreases in humid regions and increases in non-humid regions across Africa. For all SSP scenarios, the projected wetting trend per decade ranges from 0.05 to 0.25, while the drying trend per decade ranges from −0.05 to 0.25. A regional trend analysis revealed key differences in spatial pattern, with varied trend projections of wetter and drier conditions in humid and non-humid regions under all SSP scenarios. Drier conditions are expected to intensify in Southern Africa under all SSP scenarios but are projected to be more intense under either SSP3-7.0 and SSP5-8.5. In general, the projected wetter trends in humid areas may favor agricultural production and ecological conservation, and drier trends in non-humid regions may call for the possible adoption of tailor-made drought adaptation strategies and development programmes to minimize impacts.

Understanding historical and future drought patterns is crucial to acclimation and the mitigation of drought. The negative impact of China’s droughts on the social economy has attracted attention; however, there is still no comprehensive or long-term monitoring pattern for future droughts. Here we evaluated the precipitation and temperature simulation capability of Coupled Model Intercomparison Project Phase 6 (CMIP6) and evaluated the temporal and spatial pattern of droughts during 1961–2099 across China. The results show that the multi-model ensemble mean (MME) is more representative of the observed precipitation and temperatures across China than the single climate model. China experienced an overall drying trend in the historical period. After 1991, the drought frequency (DF), drought duration (DD), and drought intensity (DI) in the northwest of the Inland River Basin and in the Yangtze River Basin increased significantly. Compared with the historical period, China will suffer more frequent drought events, although the DD and DI will be weakened under SSP1-2.6 and SSP2-4.5, while China will experience longer DD and more serious drought events under SSP3-7.0 and SSP5-8.5. The Hai River Basin and Huai River Basin are expected to have more serious drought trends in summer. Compared with historical periods, the drought trend will increase by 2.9–5.7 times and 1.1–4.2 times, respectively. The results can be used for decision making regarding future drought control.

Abstract Precipitation extremes are among the most serious consequences of climate change around the world. The observed and projected frequency and intensity of extreme precipitation in some regions will greatly influence the social economy. The frequency of extreme precipitation and the population and economic exposure were quantified for a base period (1986–2005) and future periods (2016–2035 and 2046–2065) based on bias corrected projections of daily precipitation from five global climatic models forced with three representative concentration pathways (RCPs) and projections of population and gross domestic product (GDP) in the shared socioeconomic pathways (SSPs). The RCP8.5‐SSP3 scenario produces the highest global population exposure for 2046–2065, with nearly 30% of the global population (2.97 × 109 persons) exposed to precipitation extremes >10 days/a. The RCP2.6‐SSP1 scenario produces the highest global GDP exposure for 2046–2065, with a 5.56‐fold increase relative to the base period, of up to (2.29 ± 0.20) × 1015 purchasing power parity $‐days. Socioeconomic effects are the primary contributor to the exposure changes at the global and continental scales. Population and GDP effects account for 64–77% and 78–91% of the total exposure change, respectively. The inequality of exposure indicates that more attention should be given to Asia and Africa due to their rapid increases in population and GDP. However, due to their dense populations and high GDPs, European countries, that is, Luxembourg, Belgium, and the Netherlands, should also commit to effective adaptation measures.