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Urbanization is one of the pivotal aspects of socioeconomic advancement which is critically vulnerable to climatic extremes. Extreme precipitation and urbanization are largely interlinked. Estimating the extreme precipitation-induced urban area exposure is the fundamental aspect of urban risk assessment for precipitation-related floods. In this study, future urban area exposure to extreme precipitation and associated influential factors are investigated over South Asia under 1.5 °C, 2.0 °C, 3.0 °C, and 4.0 °C global warming thresholds. In this regard, we used newly released 20 up-to-date climate models outputs, and five Integrated Assessment Models (IAMs) based urban land-use products under four combined scenarios of the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) from the sixth phase of Coupled Model Intercomparison Project (CMIP6). Extreme precipitation is characterized by adopting 20-, 50-, and 100-year return periods of annual maximum daily precipitation. Results reveal a massive urban area expansion over South Asia which is the utmost by 186.4% under SSP3-7.0 than the reference period (1995-2014). The variations in projected urban areas mainly occur in Indo-Gangetic Plain (IGP) region among scenarios. In relative terms, extreme precipitation frequency and associated urban area exposure are prospective to increase with continued global warming. The exposed urban area varies 4.5- to 7.4-fold higher under different warming thresholds than the reference period. The leading increase is estimated (7.4-fold) under 4.0 °C. Notably, for global warming targets set out by the Paris Agreement (1.5 °C, and 2.0 °C), exposed urban area is intended to be 10.2% higher under 2.0 °C than 1.5 °C. Spatially, the exposed urban area will be dominant in the southeast region relative to the reference period. Importantly, the interaction effect (simultaneous change in climate-urban land) is the principal contributor to the changes in urban area exposure to extreme precipitation over South Asia. However, this study's findings strongly support the accomplishment of the Paris Agreement target and provide a scientific basis for formulating urban land-use policy interventions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Aedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre‐industrial temperatures, using multi‐model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi‐model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year. Plain Language Summary: Dengue fever, spread by Aedes aegypti and Aedes albopictus mosquitoes, is the most serious arbovirus disease affecting Asia. With global warming, mosquitoes that may carry dengue virus continue to expand in geographic range and vectorial capacity. Continued growth of mosquito populations can lead to a considerable increase in dengue exposure, especially in urban areas, increasing public health risks. Based on the occurrence data of the two mosquito species and climate observations, we explored the correlation between different climate elements and the spread of the mosquitoes. Using the outputs of 34 global climate models, we project the probability of occurrence and mosquito vectorial capacity under different warming levels in Asia. With global warming, much of Asia's area is projected to become suitable for dengue transmission. We have also estimated the future population changes in Asian metropolitan areas, to explore the combined effects of socioeconomy and climate change on exposure to dengue. As population density continues to increase and dengue epidemic potential rises, the population exposed to potential dengue fever transmission in Asian metropolitan areas is projected to surge in the future. Information conveyed in our paper can help increase preparedness for dengue outbreaks. Key Points: With global warming, a large number of areas in Asia are projected to become emerging regions suitable for dengue transmissionThe combined effects of climate and socioeconomic changes are projected to jointly lead to a surge in the population exposed to dengueAn average of 2.9 million new people are projected to be exposed to dengue fever in metropolitan areas each year in the future [ABSTRACT FROM AUTHOR]

Drought has a paramount impact on global agriculture and food security. However, the study on future cropland areas that can incur drought is inadequate. This paper uses input parameters from 7 CMIP6 models for 7 future scenarios (SSP1‐1.9, SSP1‐2.6, SSP4‐3.4, SSP2‐4.5, SSP4‐6.0, SSP3‐7.0, and SSP5‐8.5) to measure South Asian cropland exposure to drought and its underlying factors. Some defined epochs such as 2021–2040 (near‐term), 2041–2060 (mid‐term), 2081–2100 (long‐term), and 1995–2014 (reference period) are designed to explore diverse outlooks of the change. The Standardized Precipitation Evapotranspiration Index and the Run theory methods are applied to detect drought. Results indicate an intensified cropland (under SSP4‐3.4, SSP3‐7.0, and SSP5‐8.5) in the Indo‐Gangetic Plain region of South Asia, where mostly the variation occurs among scenarios and periods. Notably, the future cropland exposed to drought will increase in the 2021–2040, and 2041–2060 periods, but it intends to decline during the 2081–2100. Relatively, the exposed cropland will upturn highest by 49.2% (SSP3‐7.0) in the mid‐term period and decrease by −8.2% (SSP5‐8.5) in the end future. Spatially, distributed cropland in the central, south‐west, and portion of the northeast of South Asia are subjective to be exposed largely, but it can drop greatly across the eastern part by the end future. Importantly, the climate change effect plays a grounding role in future exposure change over the region during the near to mid‐term periods, while the cropland change effect is predominant in the long‐term perspectives. However, these findings signify the urgency of policymaking focusing on drought mitigation to ensure food security. Plain Language Summary: Estimating drought‐induced cropland exposure is the pivotal aspect of agricultural risk assessment for drought impacts. The purposes of this study are to explore (a) how much of the cropland area would be exposed to drought under SSP scenarios; and (b) the degree to which climate change and cropland change effects contribute to changes in exposure. In this regard, we conducted a multi‐model and multi‐scenario‐based analysis to reveal variations in the cropland area exposed to drought in South Asia. Among the seven scenario combinations, the highest percentage of cropland exposed to drought was found at 49.2% (SSP3‐7.0) in the mid‐term epoch (2041–2060). The climate change effect seems to be the key contributor in the near‐term to mid‐term period, whereas it is the cropland change effect long‐term. To reduce drought risk, we recommend focusing on strong climate change mitigation policy development in the near‐ and mid‐term periods at a global scale and strict land‐use management policy interventions in the long‐term period at the regional level. Key Points: Larger cropland area will experience drought in the 2040–2060 period, while it will decline by the end of 21st centuryThe exposed cropland will increase by 49.2% under SSP3‐7.0 than that of historical timeThe climate change effect is the pivotal contributor in exposure changes over South Asia [ABSTRACT FROM AUTHOR]

In the context of global climate change and urban expansion, urban residents are encountering greater rainstorm waterlogging risk. Quantifying population exposure to rainstorms is an important component of rainstorm waterlogging risk assessments. This study utilized a two-dimensional hydrodynamic model to simulate the inundation water depth and inundation area resulting from rainstorms, with return periods of 5, 10, 50, and 100 years, in the Xiong'an New Area, and overlaid the gridded population data in 2017 and in 2035 under SSP2 to assess the change in population exposure. The results show that the average inundation depth and area increase were from 0.11 m and 207.9 km2 to 0.18 m and 667.2 km2 as the rainstorm return period increased from once in 5 years to once in 100 years. The greatest water depths in the main urban areas were mainly located in the low-lying areas along the Daqing River. The total population exposed to rainstorm waterlogging for the 5-, 10-, 50-, and 100-year return periods was 0.31, 0.37, 0.50, and 0.53 million, respectively, in 2017. However, this is projected to rise significantly by 2035 under SSP2, increasing 2–4-fold compared with that in 2017 for the four return periods. Specifically, the projected population exposure is expected to be 0.7, 1.0, 1.8, and 2.0 million, respectively. The longer the return period, the greater the increase in population exposure. The proportion of the population exposed at the 0.05–0.2 m water depth to the total population exposure decreases as the return periods increases, whereas the proportion changes in the opposite direction at the 0.2–0.6 m and >0.6 m depth intervals. Spatially, high-exposure areas are concentrated in densely populated main urban regions in the Xiong'an New Area. In the future, more attention should be paid to densely populated low-lying areas and extreme recurrence rainstorm events for urban flood-risk management to ensure population safety and sustainable urban development. [ABSTRACT FROM AUTHOR]

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]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Gridded distribution of future economy plays an important role in climate change impact assessment. The trend of the output values of different industries is crucial for a variety of planning and design processes. Under the Shared Socioeconomic Pathways (SSPs) global framework, the multidimensional model and Cobb-Douglas production model with localized population and economic parameters are used to develop the annual provincial population and value-added of primary, secondary and tertiary industries in China from 2020 to 2100. The most recently implemented fertility-promoting and industrial planning policies in China are considered in our projections. We build multiple models to evaluate the impact of different types of land use on the value-added of primary, secondary and tertiary industries and then gridded the projected value-added to a 5′ × 5′ resolution, based on recorded county-level economic statistics and gridded land use. The reliability of estimations is verified against 2011–2019 statistical data and multiple published datasets. The high-resolution economic dataset is expected to contribute greatly to national and regional climate change impact, adaptation, and vulnerability studies. Measurement(s) Population • Value-added of industries • Gridded data Technology Type(s) multidimensional projection model • Cobb-Douglas production model • computational modeling technique Sample Characteristic - Location China [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) is an important factor that needs to be considered in regional water management and allocation; thus, the reasonable estimation of PET is an important topic in hydrometeorology and other related fields. There is evidence that increased CO2 concentration alters the physiological properties of vegetation and thus affects PET. In this study, changes in PET with and without the CO2 effect over China is investigated using seven CMIP6-GCMs outputs under seven shared socioeconomic pathways (SSPs) based scenarios (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5), as well as the contribution rate of CO2 on PET in different climatic regions. Changes in estimated PET based on modified Penman–Monteith (PM) method that considers the CO2 effect is compared with the traditional PM method to examine how PET quantity varies (differences) between these two approaches. The results show that the PET values estimated by the two methods explored opposite trends in 1961–2014 over entire China; it decreases with consideration of CO2 but increases without consideration of CO2. In the future, overall PET is projected to increase under all scenarios during 2015–2100 for China and its three sub-regions. PET generally tends to grow slower when CO2 is taken into account (modified PM approach), than when it is not (traditional PM method). In terms of differences in the estimated PET by the two methods, the difference between the two adopted methods increased in China and its sub-regions for the 1961–2014 period. In the future, the difference in estimated PET is anticipated to continuously increase under SSP3-7.0 and SSP5-8.5. Spatially, a much greater extent of difference is found in the arid region. Across the arid region, the PET difference is projected to be the highest at 138% in the mid-term (2041–2060) with respect to the 1995–2014 period, whereas it tends to increase slower in the long-term period (2081–2100). Importantly, CO2 is found to be the most dominant factor (−154.2% contribution) to have a great effect on PET changes across the arid region. Our findings suggest that ignorance of CO2 concentration in PET estimation will result in significant overestimation of PET in the arid region. However, consideration of CO2 in PET estimation will be beneficial for formulating strategies on future water resource management and sustainable development at the local scale. [ABSTRACT FROM AUTHOR]

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally. • PET estimates an increase of 0.68 mm/annum across Africa from 1979 to 2014. • PET is projected to increase under all scenarios, shows stronger in high forcing scenario. • Solar radiation contributed to PET in the past andmaximum temperatures in future but varies across periods and regions. • The study underscores the implications of PET estimations for drought evaluation. [ABSTRACT FROM AUTHOR]

Under climate change, land use has an essential effect on the carbon cycle in the climate change sensitive area of Qinghai-Tibet Plateau (QTP). This study about ambitions to check out the spatial and temporal patterns of carbon storage (CS) on the QTP under future land-use changes and their influencing factors. Based on historical and projected climate and land-use information and the compiled carbon-intensity dataset derived from 839 sampling points, we projected future changes in CS in the region. We projected changes of CS in the QTP in the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) periods, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We found that the total carbon storage (TCS) from 1980 to 2020 was approximately equal to 247–259 × 108 t, where C soil had the greatest contribution (about 88% of the TCS), while C above , C below and C dead accounted for approximately 5%, 5% and 2% of the TCS, respectively. Furthermore, the TCS decreased from southeast to northwest, corresponding to decreasing forest coverage and increasing unused land area in the QTP. Compared to the baseline period (1995–2014, 259 × 108 t), the CS in the QTP under the Shared Socioeconomic Pathways (SSPs) is expected to increase by 7.3–13.5%, 6.2–14.7% and 5.4–14.3% in the three future periods, respectively. Spatially, the carbon increase will be concentrated in the southeastern and northern parts of the QTP, where the land-use types are mainly forest land and grassland. Increases in precipitation and temperature are predicted to take place for the duration of the QTP in the future, driving land-use changes that are projected to result in increased area of forest land, which would increase CS. The CS in QTP is high, being influenced by land use and climatic factors, and future climate change may enhance the carbon sink capacity of the QTP. [ABSTRACT FROM AUTHOR]

Motivated by the Paris Agreement, this study aims to investigate the changes in precipitation extremes across South Asia and its five climatic zones for each 0.5 °C of warming above the pre-industrial level from 1.5 °C to 3.0 °C. In this regard, 20 global climate model outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are used. Four widely used extreme precipitation indices such as number of consecutive wet days (CWD), number of heavy precipitation days (R10mm), maximum consecutive 5-day precipitation (RX5day), and the number of very wet days (R99pTOT) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are applied to detect the extreme events. To ensure reduced uncertainty, we used bias-corrected and multi-model ensemble outputs. Results indicate that as the degree of global warming increases, the changes in magnitudes of extreme events are projected to intensify (except for CWD) and the largest growth is found under 3.0 °C in the entire domain and its five climatic zones. The polar climatic zone is anticipated to experience the highest magnitude of changes for extreme events under all the warming scenarios. The largest percentage of area with significant increase is found under the highest warming level (3.0 °C), especially for R95pTOT (95.5% of the entire domain). For an additional 0.5 °C (2.0–1.5 °C) of warming, the anticipated precipitation-related extremes will increase by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT), as mitigation targets set out in the Paris Agreement. The projected intense sea surface temperature over the Arabian Sea, and large water vapor content over South Asian landmass are expected to influence precipitation-related extreme events. Notably, considering all aspects of our analysis, R99pTOT is expected to be the most predominant extreme precipitation index across the domain with continued warming. So, our findings strongly support the Paris Agreement target to limit global warming to 1.5 °C and provide a scientific basis. • Precipitation extremes over South Asia will intensify with continued warming. • The polar climatic zone will experienceprecipitation-related extremes largely. • Extra 0.5 °C (2.0–1.5 °C) will increase the extreme event by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT). • R99pTOT is expected to be the most predominant event for South Asia. [ABSTRACT FROM AUTHOR]