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The uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the 'evapotranspiration paradox' phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future 'evapotranspiration paradox' phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the 'evapotranspiration paradox' phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I 'evapotranspiration paradoxes' that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I 'evapotranspiration paradox' phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat 'evapotranspiration paradox'‐related extremes at regional scales. [ABSTRACT FROM AUTHOR]

Seasonal precipitation and temperature predictions with global climate models that are developed based on the ocean–atmosphere interactions, contribute to the water resources management and hazard mitigation. To date, multi-model ensemble seasonal climate prediction systems, such as North American multi-model ensemble (NMME), are an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean–atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, whether the NMME real-time forecasts perform as well as the hindcasts and how the decadal variability in ocean-atmospheric teleconnections affect the prediction skill remain unclear. In this paper, based on precipitation and temperature datasets from nine models of NMME, the evaluation of forecast skills during hindcast (1982–2010) and real-time forecast (2011–2020) periods is carried out in the Yangtze River basin over China. Results show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, but the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14–51% (38–75%) and error increased by 30–31% (51–55%) for seasonal precipitation (temperature) predictions up to the sixth lead-season, and the skill decrease is larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that a better grasp of decadal variability is needed to improve the real-time climate predictions. [ABSTRACT FROM AUTHOR]

Changing climate has altered the trends and variability of precipitation and temperature globally and thereby increasing the risk of natural and social disasters, especially in coastal climatic transitional zones such as the Huaihe river basin (HRB). This paper applies the Empirical Quantile Mapping (EQM) method for bias correction and systematically evaluates the performance of the 30 Global climate models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating precipitation and temperature over the Huaihe river basin (HRB) for 1979–2014. A suitable multi-model ensemble subset (BMME) is selected based on the Euclidean Distance (ED) synthetic metric. Results show that in comparison to the baseline period 1995–2014, precipitation (temperature) over HRB is projected to increase at the rate of around 15 mm/decade (0.1 °C/decade), 16 mm/decade (0.3 °C/decade), 20 mm/decade (0.5 °C/decade), and 15 mm/decade (0.6 °C/decade) for the period 2015–2100, under Shared Socioeconomic Pathways SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. In the long-term (2081–2100), the annual precipitation is projected to increase by 32%, 27%, 35%, and 26%, under the four scenarios, respectively. The temperature is projected to remain stable or slightly decrease under SSP1-2.6 (2 °C) and SSP2-4.5 (3 °C), while will keep rising and increasing by 6 °C under SSP5-8.5 and by 4 °C under SSP3-7.0 by 2100. The isotherm and isohyet are projected to shift northwest from southeastern coastal China to inland in the future, which is likely associated with the northwestward shift of the western Pacific anticyclone in summer. [ABSTRACT FROM AUTHOR]

The impact of climate change on asphalt pavement infrastructure has become increasingly apparent. This paper investigated how climate change impacts the selection of asphalt binder for freeway pavement in China. The climate data predicted by the Global Climate Model were employed to select the appropriate performance grade for asphalt binder. The predicted air temperature converted pavement temperature, and performance grade distribution of the country was analysed. It is suggested that the performance grade of asphalt binder for freeways' pavement should be determined based on ten years' predicted climate data. This study concluded the following four major findings. Firstly, the selection of the appropriate asphalt binder performance grade needs to take into account the effects of future climate change. Secondly, the change in the standard deviation of temperature has a greater impact on the change in asphalt performance grade, as compared to the change in average air temperature. Thirdly, climate change affects the low-temperature performance grade more than the high-temperature performance grade in China. Finally, by 2050s, 15.2% of freeway asphalt pavement needs to upgrade high-temperature performance grade limit, and 17.3% of freeway asphalt pavement needs to upgrade to low-temperature performance grade limit. [ABSTRACT FROM AUTHOR]

Dams have proliferated along the Mekong, spurred by energy demands from economic development and capital from private companies. Swift dam evolution has rendered many databases outdated, in which mismatches arise from differing compilation methods. Without a comprehensive database, up-to-date spatial assessment of dam growth is unavailable. Looking at future development, hydropower potential specifically within the Mekong remains to be systematically evaluated. In this paper, we offer (1) an open-access and unified database of 1,055 dams, (2) a spatiotemporal analysis of dams on a sub-basin and country level from the 1980s to the post-2020s, and (3) a grid-based assessment of the theoretical basin-wide hydropower potential, using present-day discharge from the CaMa-Flood model (2011-2015, 0.05 degree), and future discharge from the WaterGAP2 model used for ISIMIP2b (2021-2030, 0.5 degree). The dam count of 1,055 is more than twice the largest existing database, with 608 hydropower dams generating a boom in hydropower capacity from 1,242 MW in the 1980s to 69,199 MW post-2020s. While China had the largest capacity increase from the 2000s to the 2010s (+16,854 MW), Laos has the most planned dams and the highest projected growth post-2020s (+18,223 MW). Based on present-day discharge, we estimate a basin-wide hydropower potential of 1,334,683 MW, where Laos is the highest at 514,887 MW. Based on future discharge modeled with climate change, hydropower potential could grow to over 2,000,000 MW. Laos and China are the highest at around 900,000 MW each, together forming over 80% of the total potential. Our database facilitates research on dam-induced hydrological and ecological alterations, while spatiotemporal analysis of hydropower capacity could illuminate the complex transboundary electricity trade. Through both spatiotemporal and hydropower potential evaluation, we address the current and future vulnerability of countries to dam construction, highlighting the need for better planning and management in the future hydropower hotspot Laos. The Mekong dam database is publicly available at https://doi.org/10.21979/N9/ACZIJN (Ang et al., 2023). [ABSTRACT FROM AUTHOR]

The middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semiarid and semi-humid climates. As one of the climate-sensitive regions in China, MRYR has fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses and mudslides caused by extreme precipitation to occur. However, scarceness of the high-resolution precipitation data over MRYR limits the assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this paper, we design a Residual in Residual Dense Block based network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with the generalized linear regression model and two popular deep learning-based models. The results show that the proposed RRDBNet model has a good performance on precipitation simulations, which can well reproduce the spatial-temporal characteristics of high-resolution precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with othermodels. On the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better on extreme precipitation frequency. Our results suggest that the statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models. [ABSTRACT FROM AUTHOR]

Long time series and large-scale rice calendar datasets provide valuable information for agricultural planning and field management in rice-based cropping systems. However, current regional-level rice calendar datasets do not accurately distinguish between rice seasons in China, causing uncertainty in crop model simulation and climate change impact analysis. Based on satellite remote sensing data, we extracted transplanting, heading, and maturity dates of early-, middle-, and late-season rice across China from 2003 to 2022 and established a multi-season rice calendar dataset named ChinaRiceCalendar (10.7910/DVN/EUP8EY, Liu et al., 2023). Overall, the ChinaRiceCalendar dataset shows good agreement with field-observed phenological dates of early-, middle-, and late-season rice in Chinese agricultural meteorological stations (AMSs). According to the calendar data from 2003 to 2022 in China, the transplanting dates for early-, middle-, and late-season rice shifted by + 0.7, - 0.7, and - 5.1 DOY (day of year) per decade, respectively; the heading dates for early-, middle-, and late-season rice shifted by - 0.5, + 2.7, and - 0.6 DOY per decade, respectively; the maturity dates for early-, middle-, and late-season rice shifted by - 0.7, + 3.8, and - 1.6 DOY per decade, respectively. ChinaRiceCalendar can be utilized to investigate and optimize the spatiotemporal structure of rice cultivation in China under climate and land use change. [ABSTRACT FROM AUTHOR]

• Maximising conservation benefits on state-owned land. • Quantifying land costs, ecological values and human values in an integrated climate context. • Adaptation of model parameters to research sites based on machine learning. • Ecological corridors avoiding the objective fragmentation of the Marxan model results. • Phased implementation of conservation plans. In China, especially in countries with state-owned land use rights, the loss of urban bird diversity due to rapid urbanisation can't be contained or ameliorated by equitably weighing the ecological and economic values of land. Climate change could further threaten urban bird biodiversity and ultimately lead to a "tragedy of the commons". The Beijing Ecological Cultivation Area (BECA) is one such area that has suffered from low eco-efficiency due to climate change and chaotic management. There is a need to find more effective solutions for urban bird conservation to reduce the negative impacts of rapid urbanisation while avoiding wasting more conservation costs. For this purpose, the protection priorities for BECA based on 4 global future climate models (RCP2.6, RCP4.5, RCP6.0 and RCP8.5) for the years 2041–2060, 56 species of local resident birds in the "Beijing Terrestrial Wildlife - Birds" were used as study subjects, which were identified through systematic conservation planning (scp) with combinatorial modelling (MaxEnt-Marxan). In addition, three scenarios were innovatively developed in Marxan with maximum respect for the original land use types. The eco-efficiency and eco-corridor of these three scenarios were compared and identified through ArcGis. The results of the survey show that 'Comprehensive Conservation' has the largest mean distribution area, with the results for RCP 2.6–8.5 being: 52.34%; 65.25%; 48.34%; and 45.42%. It had the highest spatial continuity, the lowest fragmentation, and the highest conservation efficiency. Moreover, We recommend a phased implementation of the conservation plans and and suggest planning options for three landform types: High-altitude mountain; Medium or Low-altitude shallow mountainous; and Water conservation areas. It is important to establish ecological corridors from the Huairou district's northern part to the Yanqing district's northeastern part and beyond. This paper quantifies the land costs, ecological values and human impact factors of the planning area in a comprehensive climate context with the theme of conserving urban bird diversity. This study aims to maximise the conservation benefits of urban bird diversity and to provide basic research for ecological conservation on state-owned land. [ABSTRACT FROM AUTHOR]

The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs' simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China. [ABSTRACT FROM AUTHOR]

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The synchronicity of rain and heat in the summer of China's monsoon region provides sufficient water and heat resources for maize growth. However, the intra-annual distribution of precipitation and the probability of extreme precipitation have been inevitably altered by the ongoing climate change, thus affecting the matching degree between precipitation and crop water requirements (MDPCWR). Evaluating the extent to which the MDPCWR will change in the future is of great importance for food security and the sustainable management of water resources. In this study, considering that different growth stages of crops have different sensitivities to water stress, the AquaCrop model was used to calculate the MDPCWR more accurately. In addition, a cumulative distribution function-transform (CDF-t) method was used to remove the bias of 11 global climate models (GCMs) under two typical emission scenarios (SSP2-4.5 and SSP5-8.5) from phase six of the Coupled Model Intercomparison Project (CMIP6). A comprehensive investigation was conducted on how maize growth, water consumption, and the MDPCWR will respond to future climate change with CO2 concentration enrichment in the Huang–Huai–Hai (3H) region in China by driving a well-tested AquaCrop model with the bias-corrected GCMs outputs. The results indicate the following: (1) The CDF-t method can effectively remove seasonal bias, and it also performs well in eliminating the bias of extreme climate events. (2) Under the SSP2-4.5 scenario, the average maximum temperature will increase by 1.31 °C and 2.44 °C in 2021–2050 and 2051–2080, respectively. The average annual precipitation will increase up to 96.8 mm/year, but it will mainly occur in the form of heavy rain. (3) The increased maize evapotranspiration rate does not compensate for the decreased crop water requirement (up to −32 mm/year), due to a shorter growth cycle. (4) The farmland cultivation layer is not able to hold a significant amount of precipitation, due to the increased frequency of heavy rains, resulting in increased irrigation water requirements for maize over the next two periods, with the maximum value of 12 mm/year. (5) Under different scenarios, the projected future MDPCWR will decrease by 9.3–11.6% due to changes in precipitation patterns and crop water requirements, indicating that it will be more difficult for precipitation to meet the water demand of maize growing in the 3H region. The results can provide comprehensive information to understand the impact of climate change on the agricultural water balance and improve the regional strategy for water resource utilization in the 3H region. [ABSTRACT FROM AUTHOR]

Understanding the impact of irrigation on the spatiotemporal characteristics of precipitation is important for the ecological stability of the arid region of Northwest China (NWC). In this study, the global climate model MPI-ESM-MR is utilized to provide the initial and lateral boundary conditions for the regional climate model RegCM4, enabling the simulation of the long-term climate of the arid NWC region under two Representative Concentration Pathways (RCPs). The study focuses on analyzing the differences in the spatiotemporal distribution, intensity, and frequency of precipitation before and after irrigation. Furthermore, the study explores the primary factors influencing changes in the spatiotemporal distribution characteristics of precipitation in the irrigation district and its downwind region. The results indicate that RegCM4 performs well in simulating the climatology and diurnal cycle of precipitation in the NWC, particularly excelling during the summer. Large-scale irrigation significantly impacts the structure of summer precipitation, leading to a notable increase in convective precipitation near the irrigation district and surrounding mountain ranges. Anomalous cyclonic circulation and enhanced vertical velocity enhancement provide dynamic conditions for increased precipitation in the irrigation district and surrounding mountainous regions. Furthermore, the evaporation of water vapor resulting from large-scale irrigation serves as an additional source of moisture, contributing to increased precipitation in the irrigation district and its downwind region. Due to the difference in climatic conditions between the western and eastern regions around the irrigated areas, the summer extreme precipitation in the west predominantly increases. At the same time, in the east, it predominantly decreases due to irrigation. The findings of this study contribute to a deeper understanding of the physical mechanisms through which agricultural irrigation influences precipitation in the arid region of the NWC, thereby providing scientific evidence for the sustainable utilization of water resources in the region. [ABSTRACT FROM AUTHOR]

In most cases, climate change projections from General Circulation Models (GCM) and Regional Climate Models cannot be directly applied to climate change impact studies, and downscaling is, therefore, needed. A large number of statistical downscaling methods exist, but no clear recommendations exist of which methods are more appropriate, depending on the application. This paper compares two different statistical downscaling methods, Pre and Pre, using the Coupled Model Intercomparison Project Phase 5 (CMIP5) datasets and station observations. Both methods include two steps, but the major difference between them is how the CMIP5 dataset and the station data used. The downscaled precipitation data are validated with observations through China and Jiangxi province from 1976 to 2005. Results show that GCMs cannot be used directly in climate change impact studies. In China, the second method Pre, which establishes regression model based on the station data, has a tendency to overestimate or underestimate the real values. The accuracy of Pre is much better than Pre based on mean absolute error, mean relative error and root mean square error. Pre fuses the mode data and station data effectively. Results also show the importance of the meteorological station data in the process of residual modification. [ABSTRACT FROM AUTHOR]

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a 'portrait' diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005. [ABSTRACT FROM AUTHOR]

Climate change in the past one hundred years has brought important influence to the natural ecosystem and social - economic system of the world and China, The impact of future climate change is also long-term and huge, and many effects are negative or adverse. Therefore, from now on we must take measures adapting to climate change to overcome the adverse effects of climate change on social - economic - ecological system, In this paper, starting from the urgency of integrated climate change risk governance, the risk of natural disasters intensifying caused by climate change is briefly described, and engineering and non engineering measures of the integrated climate change risk governance in China's high risk region are proposed, as well as recommendations on integrated risk governance in the eastern, middle and western China. [ABSTRACT FROM AUTHOR]

Heat waves, particularly humid heat waves that occur together with high temperatures and humidity, pose a severe hazard to human health in the context of global warming. In this work, daily observations from more 1500 sites are used to define dry and humid heat waves in a warm season (May to September) and assess their spatiotemporal characteristics during 1960–2015. Dry heat waves are identified with daily maximum temperatures, while humid heat waves are identified based on the wet bulb temperature, derived from both temperature and humidity. We compare dry and humid heat waves from various aspects based on multiple heat wave indices, including their frequency, duration, and intensity. Results suggest that the occurrence of both dry and humid heat wave days in China is higher in the southern than in the northern part of China, due to the higher air temperature and humidity therein. Compared to dry heat waves, humid heat waves are of higher amplitude but shorter duration. The long-term trend analysis shows that the occurrence of both dry and humid heat waves has increased overall over the last 50 years, with an especially rapid increase in the last 20 years, which may be related to China's rapid warming since the late 1980s. The future projections with multiple global climate models indicate that China will experience more frequent, stronger, and longer-duration dry and humid heat waves in the future, under both intermediate and high-emission pathways. [ABSTRACT FROM AUTHOR]

Ecosystem services (ESs) serve as a fundamental cornerstone for upholding global biodiversity and promoting human well-being. ESs trade-off and synergy are supposed to be significantly affected by climate change (CC) and land use/cover change (LULC). However, the limited availability of finely classified future land-use data and integrated landscape change models incorporating climate change scenarios has hindered our understanding of the trade-off and synergistic patterns and controls of ESs at multiple scales, particularly in arid areas. Here, a future multi-scenario ESs trade-off/collaborative assessment framework (SD-PLUS-InVEST model) for multi-scale conversion and refined land-use classification was developed by coupling the patch-generated land-use simulation (PLUS) model, system dynamics (SD) model, InVEST model, geographically weighted regression (GWR) model, optimal parameter geographical detector (OPGD) model, and structural equation model (SEM). The four ESs, namely carbon storage (CS), habitat quality (HQ), water conservation (WC), and soil conservation (SC), were assessed. Further, multi-scale ESs were evaluated under different climate change and development scenarios (i.e., the SSP1-2.6 and ecological protection scenario, SSP1-2.6-EP; SSP2-4.5 and natural development scenario, SSP2-4.5-ND; SSP5-8.5 and economic growth scenario, SSP5-8.5-EG). The results demonstrated that the arid region of northwest China (ANWC) was experiencing a significant and continuous warming trend accompanied by increased humidity. There will be a significant decrease in the areas occupied by paddy fields, natural forests, and permanent glaciers among the 24 LULC types. Conversely, there will be a substantial increase in dry land, high-coverage grassland, and urban construction land areas. According to the SSP1-2.6-EP, SSP2-4.5-ND, and SSP5-8.5-EG scenarios, the comprehensive land-use dynamic degrees were estimated to reach 2.58%, 4.08%, and 4.74%, respectively. The LULC resulting from CC exacerbates the differences in the four ESs of ANWC. In particular, CS and HQ experience significant reductions in 2100. Conversely, WC and SC show notable increases during the same period. The changes in CS, HQ, WC, and SC reach 11.36 × 108 m3, 1735.25 × 108 t, −1.29 × 108 t, and −0.009, respectively. The four ESs of CS, HQ, WC, and SC in ANWC display a synergistic relationship. This synergy is influenced by the heterogeneous spatial distribution of CS, HQ, WC, and SC, with the strongest synergy observed between CS and HQ and the weakest between CS and WC. Interestingly, the distribution differences in ESs synergy were amplified at watershed, county, and grid scales in mountainous areas, with the most significant detection differentiation occurring at the grid scale. Furthermore, the detection of spatial heterogeneity in the four ESs can be attributed to various factors. These factors include the drought index (q = 0.378), annual average precipitation (q = 0.375), economic density (q = 0.095), vegetation coverage (q = 0.262), and soil bulk density (q = 0.077). Our results highlight the importance of CC in influencing ESs. The spatial variations in ESs trade-offs and coordination at different scales, particularly the pronounced differences observed in mountainous areas, underscore the need to prioritize the conservation of arid mountainous regions in terms of future policy making. [ABSTRACT FROM AUTHOR]

The diurnal variation in precipitation and its evolution are important foundations for understanding the regional impact of climate change and the parameterization of the model. Based on the daily precipitation data set of 23 national meteorological stations during 1970–2019, the spatial and temporal distribution characteristics of precipitation concentration degree (PCD) and precipitation concentration period (PCP) in Gansu province were evaluated on daytime and nocturnal scales. The results show the following: (1) Annual precipitation ranges from 69.1 ± 24.7 mm to 578.3 ± 96.6 mm, mainly (54.1 ± 2.6%) occurring at night, and the spatial distribution of the nocturnal precipitation rate is positively (r = 0.53, p < 0.01) correlated with annual precipitation; the wetting trend (12.7 mm/10 a, p < 0.01) in the past 50 years is obvious, and is mainly dominated by the frequency of precipitation (r = 0.58, p < 0.001), with both performing better during the day. (2) Most PCD is located between 0.55 and 0.75, showing a basic distribution pattern for daytime greater than nocturnal, higher values, and stronger interannual fluctuations in arid areas; the significant decreasing trend (p < 0.05) of PCD is very clear and highly consistent, especially in the high-altitude area, and the increase in precipitation in the dry season and the improvement in precipitation uniformity in the wet season play a key role. (3) PCP often fluctuates slightly around the 39th–41st pentad, but the general rule that daytime values are smaller than night values and the interannual variability is larger in arid areas also requires special attention; PCP has shown a relatively obvious advance trend in a few regions, but this is because the prominent and complex changes in the monthly precipitation distribution pattern have not been fully reflected. Along with continuous humidification, the decrease in PCD and the advance of PCP are likely to be the priority direction of precipitation evolution in the arid region of Northwest China, especially during the day. These findings provide a new perspective for understanding regional climate change. [ABSTRACT FROM AUTHOR]

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin. [ABSTRACT FROM AUTHOR]

Projecting precipitation changes is essential for researchers to understand climate change impacts on hydrological cycle. This study projected future precipitation over the Hanjiang River Basin (HRB) based on the multimodel ensemble (ME) of six global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). An ME method using the fusion of four machine learning (ML) algorithms (random forest [RF], K‐nearest neighbors [KNN], extra tree [ET], and gradient boosting decision tree [GBDT]) was proposed in this study. The future precipitation changes were investigated during 2023–2042 (Near‐term), 2043–2062 (Mid‐term), and 2081–2100 (Long‐term) periods, with reference to the base period 1995–2014, under three integrated scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) of the Shared Socioeconomic Pathways (SSPs) and the representative concentration pathways (RCPs). The results show that: (1) the proposed ME method performs better than the ME mean and individual ML algorithms, with a correlation coefficient value reaching 0.88 and Taylor skill score reaching 0.764. (2) The precipitation under SSP5‐8.5 has the largest upward trend with the annual precipitation variation range of −9.27% to 112.84% from 2023 to 2100, followed by SSP2‐4.5 with −30.48% to 44.67%, and the smallest under SSP1‐2.6 with −37.19% to 37.78%, which show a significant trend of humidification over the HRB in the future. (3) The precipitation changes over the HRB are projected to increase over time, with the largest in the Long‐term, followed by Mid‐term, and the smallest in the Near‐term. (4) The northeastern parts of the HRB are projected to experience a large precipitation in the future, and the southeastern parts are smaller. (5) Uncertainties in the projected precipitation over the HRB still exist, which can be reduced by ME. The findings obtained in this study have important implications for hydrological policymakers to make adaptive strategies to reduce the risks of climate change. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices. [ABSTRACT FROM AUTHOR]

China features the second largest irrigation area in the world. Several modeling studies on a global scale have shown the irrigation impact in China, but no direct observational evidence has been reported. In this paper, we evaluated the impact of irrigation on temperature in Jilin province, China, using meteorological and satellite observations. The results show that the temperature difference between highly and lightly irrigated sites is statistically significant and highly correlated to the effective irrigation area and crop sown area over the past 53 years. The magnitude of the difference in maximum air temperature was almost twice that of the difference in minimum air temperature. The linear regression coefficient of the time series difference in maximum air temperature was more than three times that of the minimum air temperature. Moreover, both meteorological and satellite observations show that the temperature difference is amplified during drier years. The difference in the magnitude of daytime land surface temperature (LST), between areas with irrigation percentages of more than 50 and less than 50, is as high as 2.7 K during the driest year. Overall, this study may provide evidence that an irrigation cooling effect exists in China. [ABSTRACT FROM AUTHOR]

Climate change is one of the biggest risks of our time for human society. Anticipating the future under the climate change and identifying effective climate mitigation strategies are very challenging tasks. Considering the climate change has led to significant temperature rise, Zhang et al. profile an effort to estimate the changes in electricity consumption due to temperature fluctuation resulted from climate change at the county scale in rural China. [Extracted from the article]

Understanding future climate change is crucial for securing and managing water supplies necessary for agricultural activities in China, particularly in rainfed agriculture regions. This paper evaluates the projected climate change for nine major agricultural zones in China, using ensembles of regional climate models (RCMs) simulated under the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II) framework. The simulations were conducted for two climate scenarios (RCP2.6 and RCP8.5) at a 25 km grid spacing, with three RCMs (RegCM4, REMO, and WRF) driven by multiple CMIP5 global climate models (GCMs). The study compares the RCM simulations to the driving CMIP5 and the recent CMIP6 ensembles, focusing on surface temperature, precipitation, and surface relative humidity for the present day (1981–2010), mid-future (2036–2065), and far future (2070–2099) periods. The model evaluations indicate that the GCM and RCM simulations are consistent with observations, with RCMs showing better performance than the driving GCMs. The results show that both GCMs and RCMs project increased temperature and precipitation in most agricultural zones of China in the future, with the CMIP6 ensembles projecting the largest temperature increase and the CORDEX-EA-II ensemble showing the finest spatial details. Relative humidity is projected to decrease over major rice-producing areas of South China in the CORDEX-EA-II, CMIP5, and CMIP6 ensembles. This study provides valuable information to support climate change adaptation and mitigation efforts in China, particularly in the agricultural sectors. • CORDEX-EA-II simulations demonstrate better model performance over China. • Temperature and precipitation are projected to increase in the future. • Relative humidity is projected to decrease over South China. [ABSTRACT FROM AUTHOR]

Climate change and human activities significantly impact the hydrological cycle, particularly in regions with numerous large-scale reservoirs. Recognizing the limitations of the reservoir module in the original SWAT model, this study presents an improved reservoir module based on a dispatch function to enhance runoff simulation. Its performance is validated by simulating daily runoff in the Jinsha River Basin, China. The scenario simulation approach is employed to quantitatively analyze the influences of climate change and human activities on runoff. And downscaled Global Climate Models (GCMs) are utilized to predict runoff for the next three decades. The results show that (1) the improved SWAT model outperforms the original model in runoff simulation; (2) during the test period, reservoir regulations caused a reduction of 26 m3/s in basin outlet runoff, while climate change led to an increase of 272 m3/s; and (3) future changes in basin outlet runoff over the next 30 years exhibit a high level of uncertainty, ranging from −5.6% to +11.0% compared to the base period. This study provides valuable insights into the hydrological impacts of climate change and human activities, highlighting the importance of incorporating an improved reservoir module in hydrological modeling for more accurate predictions and assessments. [ABSTRACT FROM AUTHOR]

As the planet warms, the atmosphere's water vapor holding capacity rises, leading to more intense precipitation extremes. River floods with high peak discharge or long duration can increase the likelihood of infrastructure failure and enhance ecosystem vulnerability. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change are still poorly understood. This study employs a bivariate framework to quantify changes in flood risks and their socioeconomic impacts in China between the past (1985–2014) and future (2071–2100) in 204 catchments. Future daily river streamflow is projected by using a cascade modeling chain based on the outputs of five bias‐corrected global climate models (GCMs) under three shared socioeconomic CMIP6 pathways (SSP1‐26, SSP3‐70, and SSP5‐85), a machine learning model and four hydrological models. We also utilize the copula function to build the joint distribution of flood peak and duration, and calculate the joint return periods of the bivariate flood hazard. Finally, the exposure of population and regional gross domestic product to floods are investigated at the national scale. Our results indicate that flood peak and duration are likely to increase in the majority of catchments by 25%–100% by the late 21st century depending on the shared socioeconomic pathway. China is projected to experience a significant increase in bivariate flood risks even under the lowest emission pathway, with 24.0 million dollars/km2 and 608 people/km2 exposed under a moderate emissions scenario (SSP3‐70). These findings have direct implications for hazard mitigation and climate adaptation policies in China. Plain Language Summary: Our planet is becoming increasingly vulnerable to the dangers of river flooding, frequently causing significant environmental damage and economic losses. In a warming climate, precipitation extremes are projected to intensify, increasing flood risk as well as its environmental impacts. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change is little studied, particularly in China. By the late 21st century, the exposure of the population and China's total economic outputs/prospects is projected to reach 24.0 million dollars/km2 and 608 people/km2, even under considerable greenhouse gas emission. In order to mitigate the potential socioeconomic exposure and vulnerability, the implementation of the Sendai Framework for Disaster Risk Reduction is necessary and urgent. Our findings provide valuable information for mitigating hazards and developing climate adaptation policies in China. Key Points: We project future daily river streamflow using a hybrid approach that combines machine learning, hydrological models and climate modelsWe evaluate shifts in future flood risks within a bivariate copula‐based frameworkChina might experience a doubling of flood risk by the late 21st century under SSP3‐70, with over a billion people being affected [ABSTRACT FROM AUTHOR]

The relationship between maximizing forest revenue and reducing environmental pollution has been a challenging one. It is every country's responsibility to protect its forest reserves and mitigate climate change. Studies on the relationship between forest economic models and climate change are limited, and most of them focus on maximizing forestry products. This study aims at filling the gaps and makes scientific contributions by providing a detailed account of various economic models and their correlations with climate change, as well as identifying the ecological footprint of forest products, fossil fuel consumption, forest cover, foreign direct investment, economic growth, and population in terms of carbon dioxide (CO2) emissions. In this study, we observed that most forest economic models focus on forest profit maximization and disregard climate impact. The empirical results suggest that the ecological footprint of forest products increases CO2 emissions. In addition, forest cover helps to reduce CO2 emissions. A case study of China's tremendous growth and the associated CO2 emissions levels reported a recent decrease in such levels, largely due to an increase in forest cover. Although these findings are not exhaustive, they provide new insights into forestry economic models and the impact of climate change, offering theoretical and practical implications for future reference and forest governance. [ABSTRACT FROM AUTHOR]

Understanding future dry and wet changes under global climate change is of great regional significance in China. Based on six Global Climate Models (GCM) from the Coupled Model Intercomparison Project phase 6 (CMIP6), we project the changes of dry‐wet climate regions in China in the 21st century under the three SSP‐RCP (shared socioeconomic pathway‐representative concentration pathway) scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), and further identify the key climatic factors that lead to the transition of dry‐wet climate regions. The results show the area of humid and subhumid regions in China will show an upward trend in the future, while the area of semiarid and arid regions will show a downward trend. Especially under the SSP5‐8.5 scenario, the area of the subhumid region has the largest increase trend (0.042% per year), and the area of the arid region has the largest decline trend (−0.048% per year). The three main transfer directions of dry‐wet climate regions are from arid to semiarid regions, from semiarid to subhumid regions, and from subhumid to humid regions in the future. Due to the future extensive wetting trend in the north, the future dry‐wet climate regions will shift to wetter types in Tibet, North China, the Qinling‐Huaihe regions and the northeast. In addition, the key climatic factors for the transition of dry‐wet climate regions are mainly precipitation, maximum temperature and solar radiation. The increase of precipitation plays a dominant role for areas projected to change to wetter types, and the increase of potential evapotranspiration (PET) plays a dominant role for areas projected to change to drier types. The PET changes in changed areas of dry‐wet climate regions are mainly dominated by maximum temperature and solar radiation. Our research has important implications for future water resource management and reconsideration of climate mitigation and adaptation policies. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) downstream of the Tibetan Plateau (TP) exhibit unique precipitation features. These MCSs can have damaging impacts and there is a critical need for improving the representation of MCSs in numerical models. However, most global climate models are typically run at resolutions that are too coarse to reasonably resolve MCSs, and it is still unclear how well higher-resolution global models can reproduce the precipitation characteristics of MCSs. In this study, the sensitivity of MCSs simulated by a global high resolution (~ 10 km), atmosphere-only climate model to different treatments of convection (with and without parametrized convection, and a hybrid representation of convection) have been investigated. The results show that explicit convection (i.e., non-parameterized) can better reproduce the observed pattern of MCS precipitation over the East Asian Summer Monsoon region. In general, explicit convection better simulates the diurnal variability of MCSs over the eastern China, and is able to represent the distinctive diurnal variations of MCS precipitation over complex terrain particularly well, such as the eastern TP and the complex terrain of central-northern China. It is shown that explicit convection is better at simulating the timing of initiation and subsequent propagating features of the MCS, resulting in better diurnal variations and further a better spatial pattern of summer mean MCS precipitation. All three experiments simulate MCS rainfall areas which are notably smaller than those in observations, but with much stronger rainfall intensities, implying that these biases in simulated MCS morphological characteristics are not sensitive to the different treatment of convection. [ABSTRACT FROM AUTHOR]

Climate change impacts ocean nutrient availability and will likely alter the marine food web. While climate models predict decreased average ocean productivity, the extent of these changes, especially in the marginal seas upon which large human populations depend, is not well understood. Here, we reconstructed changes in seawater phosphate concentration and nitrate source over the past 400 years, which reveals a more than 50% decline in residence time of seawater phosphate, and 8%–48% decline in subsurface nitrogen supply following the coldest period of Little Ice Age. Our data indicates a link between surface ocean nutrient supply and the East Asian Summer Monsoon strength in an economically important marginal sea. As climate models predict that the East Asian Summer monsoon will strengthen in the future, our study implies that surface ocean primary productivity may increase in the South China Sea, contrary to the predicted decrease in global average ocean productivity. Plain Language Summary: Global climate models predict that surface ocean nutrient concentrations will significantly decrease over the next 100 years as our climate changes. However, in the world's marginal seas, which human populations depend on most heavily for food security, the surface ocean biogeochemical changes are still difficult to predict with global‐scale models. Here, using coral‐based paleoclimate proxies, we reconstruct changes to surface phosphate concentration and to sources of surface nitrogen over the past four centuries in the South China Sea (SCS). Combined with box model simulations, our data reveal a more than 50% decline in phosphate residence time and an 8%–48% decline in subsurface nitrate supply from the 1600s to present. Such a decline implies a significant decrease in primary productivity and likely a community shift toward nitrogen‐fixing phytoplankton. Importantly, our data indicate that the decrease in nutrient supply was driven by weakened monsoon‐induced upwelling, demonstrating strong ties between nutrient supply and monsoon strength in the SCS. Climate change is predicted to strengthen the East Asian Summer Monsoon, and our results suggest that primary productivity in this economically important region may increase, contrary to the widely predicted decreases in average global ocean productivity. Key Points: Coral skeletal phosphorus content with scattered nitrogen isotopes is reconstructed over the past 400 yearsNutrient supply declined up to 48% since the Little Ice Age in the western South China SeaThe coral record from links nutrient supply to monsoon wind, implying higher productivity under a stronger monsoon in the future [ABSTRACT FROM AUTHOR]

Nature-based Solutions (NbS) can undoubtedly play a significant role in carbon neutrality strategy. Forests are a major part of the carbon budget in terrestrial ecosystems. The possible response of the carbon balance of southwestern forests to different climate change scenarios was investigated through a series of simulations using the forest ecosystem carbon budget model for China (FORCCHN), which clearly represents the influence of climate factors on forest carbon sequestration. Driven by downscaled global climate model (GCM) data, the FORCCHN evaluates the carbon sink potential of southwestern forest ecosystems under different shared socioeconomic pathways (SSPs). The results indicate that, first, gross primary productivity (GPP), ecosystem respiration (ER) and net primary productivity (NPP) of forest ecosystems are expected to increase from 2020 to 2060. Forest ecosystems will maintain a carbon sink, but net ecosystem productivity (NEP) will peak and begin to decline in the 2030s. Second, not only is the NEP in the SSP1-2.6 scenario higher than in the other climate change scenarios for 2025–2035 and 2043–2058, but the coefficient of variation of the NEP is also narrower than in the other scenarios. Third, in terms of spatial distribution, the carbon sequestration potential of northwest and central Yunnan is significantly higher than that of other regions, with a slight upward trend in NEP in the future. Finally, GPP and ER are significantly positively correlated with temperature and insignificantly correlated with precipitation, and the increasing temperature will have a negative and unstable impact on forest carbon sinks. This study provides a scientific reference for implementing forest management strategies and achieving sustainable development. [ABSTRACT FROM AUTHOR]

The uncertainty of the climatic effect of Black carbon (BC) remains large. One critical uncertainty source that needs to be captured is BC aging. Here we use the Community Atmosphere Model version 6 (CAM6) configured with the four‐mode version of the Modal Aerosol Module (MAM4) to evaluate the modeled BC aging process with recent laboratory and in‐situ measurements over China. As revealed by the comparison of BC aging timescale and number fraction of aged BC against recent measurements, the modeled condensation aging timescale is estimated to be about 0.8 hr (17%) faster than the chamber measurement, and the diurnal variations of modeled BC aging degree are typically higher than observations mainly due to the fast increase in modeled BC aging degree during daytime. Further analysis shows that the condensation aging dominates (>70%) BC aging across China. More specifically, the condensation of secondary organic aerosol (SOA) vapor contributes most to BC aging over China. Slowing down BC aging increases the modeled surface BC concentration over remote Western China and BC burden, but hardly changes surface BC concentration over Eastern China. Our results suggest that BC aging representation in the MAM4 needs to be further improved toward slowing down the BC aging rate, especially the condensation aging by SOA, to improve the BC simulation over remote areas and its impact on BC transport in MAM4. Plain Language Summary: Black carbon (BC) has a warming effect on climate. Currently, significant divergences in simulated BC burden and optical properties have been shown among global climate models (GCMs), and hence BC radiative forcing remains largely uncertain. BC aging is one of the key processes that contribute to uncertainties in BC simulation. In this study, we evaluate the BC aging process in a global climate model with recent laboratory and in‐situ measurements over China. Our study illustrates that BC aging is too fast in the model compared with observations, and the dominant process for modeled BC aging is SOA vapor condensation over China. We further show how different BC aging rates might affect modeled surface BC concentration and BC burden. Our results suggest that further measurements are needed to better understand BC aging mechanism to improve BC aging in GCMs. Key Points: BC aging is too fast in MAM4 compared against measured BC condensation aging timescale and number fraction of aged BCSOA vapor condensation plays a dominant role in modeled BC aging process over ChinaSlowing down BC aging increases surface BC over remote Western China and BC burden, but hardly changes surface BC over Eastern China [ABSTRACT FROM AUTHOR]

The Asian elephant (Elephas maximus Linnaeus) is a globally endangered species, an internationally protected species, and a first-class protected animal in China. However, future climate change and human activities exacerbate the instability of its habitat range, leading to a possible reduction in the range. By using multi-source remote sensing data and products, as well as climate change models, including ASTER GDEM v3, Landsat8 OLI image and ClimateAP, we examined the effects of ecological factors related to climate and natural and anthropogenic influences on the distribution of Asian elephants in Sipsongpanna. Multiyear elephant field tracking data were used with a MaxEnt species distribution model and the climate model. First, the distribution of Asian elephants in potentially suitable areas in Sipsongpanna was simulated under current climatic conditions without considering human activities. The predicted distribution was verified by existing Asian elephant migration trajectories. Subsequently, the distribution of potentially suitable areas for Asian elephants in Sipsongpanna was simulated under two climate change scenarios (RCP4.5, RCP8.5) in three periods (2025, 2055, and 2085). The changes in potentially suitable areas for Asian elephants in Sipsongpanna were analyzed under multiple climate change scenarios for the current (2017) and different future periods by considering the effects of human activities. The results show the following: (1) under anthropogenic interference (AI), the optimal MaxEnt model has a high prediction accuracy with the area under the curve (AUC) of 0.913. The feature combination (FC) includes linear, quadratic, and threshold features, and the regularization multiplier (RM) is 2.1. (2) Jackknife analyses of the non-anthropogenic interference (NAI) and anthropogenic interference (AI) scenarios indicate that topography (altitude (Alt)), temperature (mean warmest month temperature (MWMT)), and precipitation (mean annual precipitation (MAP)) are the top three factors influencing the distribution of Asian elephants. (3) The total area suitable for Asian elephants under current climate conditions and AI accounts for 46.35% of the total area. Areas of high suitability (occurrence probability >0.5) are located in Jinghong City in central Sipsongpanna and Mengla County in southeastern Sipsongpanna. Among them, the minimum habitat range and ecological corridors are mainly located in Mengman Town, Mohan Town, Mengla Town, Mengban Township, Dadugang Township, and Mengwang Township. (4) The change in potentially suitable areas for Asian elephants between current and future conditions is small under AI and large under undisturbed conditions. [ABSTRACT FROM AUTHOR]

All current global climate models (GCMs) utilize only grid-averaged surface heat fluxes to drive the atmosphere, and thus their subgrid horizontal variations and partitioning are absent. This can result in many simulation biases. To address this shortcoming, a novel parameterization scheme considering the subgrid variations of the sensible and latent heat fluxes to the atmosphere and the associated partitioning is developed and implemented into the National Center for Atmospheric Research (NCAR) Climate Earth System Model 1.2 (CESM1.2). Compared to the default model, in addition to the improved boreal summer precipitation simulation over eastern China and the coastal areas of the Bay of Bengal, the long-standing overestimations of precipitation on the southern and eastern margins of the Tibetan Plateau (TP) in most GCMs are alleviated. The improved precipitation simulation on the southern margin of the TP is from suppressed large-scale precipitation, while that on the eastern edge of the TP is due to decreased convective precipitation. Moisture advection is blocked toward the southern edge of the TP, and the anomaly of anticyclonic moisture transport over northern China extends westward, suppressing local convection on the eastern edge of the TP. The altered large-scale circulation in the lower atmosphere resulting from anomalous heating and cooling in the planetary boundary layer is responsible for the change in moisture transport. The performance of other key variables (e.g., surface energy fluxes, clouds and 2 m temperature) is also evaluated thoroughly using the default CESM1.2, the new scheme and the scheme stochastically allocating the subgrid surface heat fluxes to the atmosphere (i.e., without subgrid partitioning included). This study highlights the importance of subgrid surface energy variations and partitioning to the atmosphere in simulating the hydrological and energy cycles in GCMs. [ABSTRACT FROM AUTHOR]

The biophysical effects of vegetation changes are important in determining future climate changes using climate model. However, compared to observations, model has biases in energy exchange between vegetation and the lower atmosphere modulated by leaf area index and albedo. In this study, land-use induced anthropogenic influences, estimated as the differences between present land-use and idealized natural vegetation, on near-surface temperature were investigated using a regional climate model. Results show that present land-use transitions over China brings a cooler summer and winter accompanied by reduced diurnal temperature ranges by 0.11 °C and 0.25 °C respectively, which are mainly determined by the overwhelming increased evaporation and latent heat flux in summer and reduced net radiation in winter. Three vegetation pairs (i.e., forest and cropland, grassland and cropland, grassland and forest) were selected using observational datasets to evaluate vegetation induced climatic impact without atmospheric feedbacks across various climatic regimes. Albedo led absorbed radiation plays a dominate role in middle to north region while both LAI and albedo are significant below 30° N for latitudinal temperature changes between cropland and forest transitions. Model results have inconsistencies with observations on temperature trends caused by vegetation pairs, indicating summer cropland and forest over southern China is the most sensitive to the atmosphere conditions and forest and grassland pair is the least. These findings demonstrate the heterogeneous biophysical effect of vegetation in different climate zones and imply that a region-oriented parameterization of vegetation types should be applied in the land surface model to reduce uncertainties in future climate prediction. [ABSTRACT FROM AUTHOR]

Soil moisture is an important parameter required for agricultural drought monitoring and climate change models. Passive microwave remote sensing technology has become an important means to quickly obtain soil moisture across large areas, but the coarse spatial resolution of microwave data imposes great limitations on the application of these data. We provide a unique soil moisture dataset (0.05 ∘ , monthly) for China from 2002 to 2018 based on reconstruction model-based downscaling techniques using soil moisture data from different passive microwave products – including AMSR-E and AMSR2 (Advanced Microwave Scanning Radiometer for Earth Observing System) JAXA (Japan Aerospace Exploration Agency) Level 3 products and SMOS-IC (Soil Moisture and Ocean Salinity designed by the Institut National de la Recherche Agronomique, INRA, and Centre d'Etudes Spatiales de la BIOsphère, CESBIO) products – calibrated with a consistent model in combination with ground observation data. This new fine-resolution soil moisture dataset with a high spatial resolution overcomes the multisource data time matching problem between optical and microwave data sources and eliminates the difference between the different sensor observation errors. The validation analysis indicates that the accuracy of the new dataset is satisfactory (bias: - 0.057, - 0.063 and - 0.027 m3m-3 ; unbiased root mean square error (ubRMSE): 0.056, 0.036 and 0.048; correlation coefficient (R): 0.84, 0.85 and 0.89 on monthly, seasonal and annual scales, respectively). The new dataset was used to analyze the spatiotemporal patterns of soil water content across China from 2002 to 2018. In the past 17 years, China's soil moisture has shown cyclical fluctuations and a slight downward trend and can be summarized as wet in the south and dry in the north, with increases in the west and decreases in the east. The reconstructed dataset can be widely used to significantly improve hydrologic and drought monitoring and can serve as an important input for ecological and other geophysical models. The data are published in Zenodo at 10.5281/zenodo.4738556 (Meng et al., 2021a). [ABSTRACT FROM AUTHOR]

Comparison and quantification of different uncertainties of future climate change involved in the modeling of a hydrological system are highly important for both hydrological modelers and policy-makers. However, few studies have accurately estimated the relative importance of different sources of uncertainty at different spatiotemporal scales. Here, a hierarchical sensitivity analysis framework (HSAF) incorporated with a variance-based global sensitivity analysis is developed to quantify the spatiotemporal contributions of different uncertainties in hydrological impacts of climate change in two different climatic (humid and semiarid) basins in China. The uncertainty sources include three emission scenarios (ESs), 20 global climate models (GCs), three hydrological models (HMs), and the associated sensitive hydrological parameters (PAs) screened and sampled by the Morris and Latin hypercube sampling methods, respectively. The results indicate that the overall trend of uncertainty is PA > HM > GC > ES, but their uncertainties have discrepancies in projections of different hydrological variables. The HM uncertainty in annual and monthly discharge projections is generally larger than the PA uncertainty in the humid basin than semiarid basin. The PA has greater uncertainty in extreme hydrological event (annual peak discharge) projections than in annual discharge projections for both basins (particularly for the humid basin), but contributes larger uncertainty to annual and monthly discharge projections in the semiarid basin than humid basin. The GC contributes larger uncertainty in all the hydrological variables projections in the humid basin than semiarid basin, while the ES uncertainty is rather limited in both basins. Overall, our results suggest there is greater spatiotemporal variability of hydrological uncertainty in more arid regions. [ABSTRACT FROM AUTHOR]

Climate change has substantial impacts on regional hydrology in the major river basins. To figure out such latent hydrological impacts of changing climate, more reliable hydrological simulations are imperative. In this study, we evaluated the impacts of climate change on hydrological regime in the Upper Yangtze River Basin based on four downscaled and bias-corrected Global Climate Model outputs from Coupled Model Intercomparison Project Phase 5 under four Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) driving three hydrological models. Two model evaluation approaches were applied: simple and comprehensive. The comprehensive approach was used to evaluate models in the historical period, optimizing objective function at four gauges, and hydrological models were weighted for impact assessment based on their performance. In such a way, projected streamflow time series are obtained under different emission scenarios. Results show that the annual average discharge is projected to increase by 4.1–10.5% under the RCP scenarios at the end of twenty-first century relative to the reference period (1970–1999). Moreover, the high flow is projected to increase and the low flow to decrease indicating a higher probability of flood and drought occurrence in the basin. The severity of floods and droughts may increase. In comparison with the simple one-site model evaluation approach, the comprehensive method reveals that the anticipated extreme flow events would be less severe, and annual mean discharge slightly lower. The projected results imply that application of the comprehensive model evaluation approach could narrow the simulated spreads of projections significantly, and might provide more credible results. [ABSTRACT FROM AUTHOR]

Background: Global warming has brought many negative impacts on terrestrial ecosystems, which makes the vulnerability of ecosystems one of the hot issues in current ecological research. Here, we proposed an assessment method based on the IPCC definition of vulnerability. The exposure to future climate was characterized using a moisture index (MI) that integrates the effects of temperature and precipitation. Vegetation stability, defined as the proportion of intact natural vegetation that remains unchanged under changing climate, was used together with vegetation productivity trend to represent the sensitivity and adaptability of ecosystems. Using this method, we evaluated the vulnerability of ecosystems in Southwestern China under two future representative concentration pathways (RCP 4.5 and RCP 8.5) with MC2 dynamic global vegetation model. Results: (1) Future (2017–2100) climate change will leave 7.4% (under RCP 4.5) and 57.4% of (under RCP 8.5) of areas under high or very high vulnerable climate exposure; (2) in terms of vegetation stability, nearly 45% of the study area will show high or very high vulnerability under both RCPs. Beside the impacts of human disturbance on natural vegetation coverage (vegetation intactness), climate change will cause obvious latitudinal movements in vegetation distribution, but the direction of movements under two RCPs were opposite due to the difference in water availability; (3) vegetation productivity in most areas will generally increase and remain a low vulnerability in the future; (4) an assessment based on the above three aspects together indicated that future climate change will generally have an adverse impact on all ecosystems in Southwestern China, with non-vulnerable areas account for only about 3% of the study area under both RCPs. However, compared with RCP 4.5, the areas with mid- and high-vulnerability under RCP 8.5 scenario increased by 13% and 16%, respectively. Conclusion: Analyses of future climate exposure and projected vegetation distribution indicate widespread vulnerability of ecosystems in Southwestern China, while vegetation productivity in most areas will show an increasing trend to the end of twenty-first century. Based on new climate indicators and improved vulnerability assessment rules, our method provides an extra option for a more comprehensive evaluation of ecosystem vulnerability, and should be further tested at larger spatial scales in order to provide references for regional, or even global, ecosystem conservation works. [ABSTRACT FROM AUTHOR]

Since the Last Glacial Maximum (LGM), the global climate has experienced several stages, such as cold and warming events, which provide an ideal model for evaluating climate change in the future. Based on the pollen records in Northeast (NE) China, the vegetation pattern during special periods since the LGM was reconstructed in this work. During the LGM (approximately 18,000 cal yr BP), the steppes expanded rapidly in NE China, and a cold-dry meadow-steppe developed on the Songnen Plain. The Liaohe Plain and the Hulun Buir Plateau were occupied by a steppe-desert, with forest-steppe vegetation grown in the central and southern plains; there were cold-dry coniferous forests and mixed conifer-broadleaf forests in mountainous areas. In the early Holocene (10,000–9,000 cal yr BP), Changbai mountain (CBM) forests thrived in the eastern hilly area and the Sanjiang Plain, while the central region was dominated by steppes, and warm-temperate broadleaf forests developed northward. During the Holocene warm period (approximately 6,000 cal yr BP), CBM forests and cold-temperate coniferous forests developed in the north, while spruce-fir forests developed in the eastern Xiao Hinggan Mountains and the Sanjiang Plain. The distribution centre of deciduous broadleaf forests migrated to the south of the Changbai Mountains and the Liaodong Peninsula. The isolated woodlands increased on the Songnen Plain and the meadow-steppes expanded to the Liaohe Plain. Therefore, the increase in temperature leads to the increase of monsoon precipitation in NE China, which is beneficial to the development of warm-temperate forest vegetation. The increase of summer monsoons and precipitation caused by climate warming may be the main reason for the improved plant load. [ABSTRACT FROM AUTHOR]

Background: The medicinal plants of Radix et Rhizoma Nardostachyos include Nardostachys jatamansi and N. chinensis. Traditionally, the two plants have been used to treat many diseases. Because of their special aroma, they are also commonly used in the food and cosmetics industry. Recently, N. jatamansi and N. chinensis have been overexploited due to their economic importance, resulting in a sharp decline in their wild resources. Predicting potential distributions of the genus Nardostachys under different climate scenarios and understanding its preferred habitat are of great significance for their conservation, artificial cultivation, and assessment of their value. Methods: The Maxent model was used to predict the potential geographical distributions of the genus Nardostachys under current and future climatic conditions based on two representative concentration pathways (RCP2.6 and RCP8.5) for the 2050s and 2070s. These data were used to study the effects of climate variables. Results: The results show that the potential distribution of the two species will increase, thus more suitable habitats will be present in China. The suitable habitat for N. chinensis presents a relatively stable growth compared to N. jatamansi. In addition, precipitation plays a crucial role in modeling the effects of climate change on the genus Nardostachys. This study provides theoretical guidance for the cultivation of N. chinensis. [ABSTRACT FROM AUTHOR]

This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score (TSC) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio (SNR). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models. The domains used in the study (SOUTH: 110°–140°E, 20°–30°N, MIDDLE: 110°–140°E, 30°–40°N), NORTH: 110°–140°E, 40°–50°N, EA: 110°–140°E, 20°–50°N) [ABSTRACT FROM AUTHOR]

The spatial and temporal pattern of sub-daily temperature change in mainland China was analysed for the period from 1973 to 2011 using a 3-hourly dataset based on 408 stations. The increase in surface air temperature was more significant by night between 1973 and 1992, with the fastest upward trend around local midnight being about 0.27 °C/decade, while it was more significant by day between 1992 and 2011, with the fastest upward trend being about 0.46 °C/decade in mid-late morning. The season with rapid temperature increase also shifted from winter in 1973-1992 (the largest increase happened near midnight in December, 0.75 °C/decade) to spring in 1992-2011 (the largest increase happened at in the early afternoon in March, 0.82 °C/decade). The change in the spatial distributions of the sub-daily temperature trends shows that Northeast China warmed more significantly in 1973-1992 than elsewhere, but it cooled in 1992-2011, when Southwest China was the new focus of temperature increase whereas it had previously been cooling. A preliminary analysis of the possible causes implies that changes in solar radiation, cloud cover, aerosols and the observational environments near the stations might have contributed to these observed temperature changes. [ABSTRACT FROM AUTHOR]

Water resources from rivers are essential to humans. The discharge of rivers is demonstrated to be significantly affected by climate change in the literature, particularly in the boreal and subboreal climate zones. The Da Hinggan Mountains in subboreal northeast China form the headwaters of the Heilongjiang River and the Nenjiang River, which are important water resources for irrigation of downstream agriculture and wetlands. In this study, long-term (44 years) hydrologic, climate and forest dynamics data from the Tahe were analyzed using the soil and water assessment tool (SWAT) model to quantify the effects of climate and forest change on runoff depth. Meanwhile, downscaled precipitation and temperature predictions that arose from global climate models (GCMs) under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5) were forced using the SWAT model to investigate the climate change impacts on the Tahe River flows in the future. The results indicated that compared with the 1972–1982 period, the forest biomass in the 1984–1994 period was reduced by 17.6%, resulting in an increase of 16.6% in mean annual runoff depth. On the contrary, with reforestation from the 1995–2005 period to the 2006–2016 period, the mean forest biomass was increased by 9.8%, resulting in the mean runoff depth reduction of 11.9%. The tree species composition shift reduced mean annual runoff depth of 13.3% between the 1984–1994 period and the 2006–2016 period. Compared with base years (2006–2016), projections of GCM in the middle of the 21st century indicated that both mean annual temperature and precipitation were expected to increase by −0.50 °C and 43 mm under RCP 2.6, 0.38 °C and 23 mm under RCP 4.5, 0.67 °C and 36 mm under RCP 6.0 and 1.00 °C and 10 mm under RCP 8.5. Simulated results of the SWAT model showed that annual runoff depth would increase by 18.1% (RCP 2.6), 11.8% (RCP 4.5), 23.6% (RCP 6.0), and 11.5% (RCP 8.5), compared to the base years. Such increased runoff was mainly attributed to the increase in April, July, August, September and October, which were consistent with the precipitation prediction. We concluded that the future climate change will increase the water resources from the river, thereby offsetting the possible decline in runoff caused by the forest recovery. The findings of this study might be useful for understanding the impacts of climate and forest change on runoff and provide a reasonable strategy for managers and planners to mitigate the impact of future climate change on water resources in the subboreal forested watersheds. [ABSTRACT FROM AUTHOR]

Climate change significantly influences changes in ecological phenomena and processes, such as species distribution and phenology, thus accelerating the rate of species extinction or prosperity. Climate change is considered to be one of the most important threats to global biodiversity in the 21st century and will pose significant challenges to biodiversity conservation in the future. The use of niche modelling to predict changes in the suitable distribution of species under climate change scenarios is becoming a hot topic of biological conservation. In this study, we use data from China's National Forest Continuous Inventory as well as specimen collection data of Cunninghamia lanceolata (Lamb.) Hook to run optimized Maxent models to predict potential suitable distribution of the species in the present day, 2050s, and 2070s under different climate change scenarios in China. In the modeling process, the most important uncorrelated variables were chosen, and the sample-size-adjusted Akaike information criterion (AICc) was used to select the optimal combination of feature type and regularization multiplier. Variable selection reduced the number of variables used and the complexity of the model, and the use of the AICc reduced overfitting. Variables relating to precipitation were more important than temperature variables in predicting C. lanceolata distribution in the optimal model. The predicted suitable distribution areas of C. lanceolata were different for the different periods under different climate change scenarios, with the centroids showing a degree of northward movement. The suitable distribution area is predicted to become more fragmented in the future. Our results reveal the climate conditions required for the suitable distribution of C. lanceolata in China and the likely changes to its distribution pattern in the future, providing a scientific basis for the sustainable management, protection, and restoration of the suitable habitat of this economically important tree species in the context of climate change. [ABSTRACT FROM AUTHOR]

Dryland montane forests conserve water for people living in the fluvial plains. The fate of these forests under climate warming is strongly affected by local environmental factors. The question remains of how internal factors contribute to climate change impacts on forest growth in these regions. Here, we investigated tree ring records for similar-aged stand-grown trees and their neighboring open-grown trees at elevation in a dryland montane forest (Picea crassifolia Kom.) in northwestern China. The growth rate of open-grown trees is much higher than their neighboring stand-grown trees across the entire elevation gradient, and the lower the altitude, the greater the difference. Open-grown trees at different elevations showed similar growth patterns, as tree growth at all sites was accelerated over time. In contrast, growth patterns of stand-grown trees were divergent at different altitudes, as growth at high elevations (3100–3300 m a.s.l.) was accelerated, whereas growth at low elevations (2700–2900 m a.s.l.) became stable after the year 1990. Analysis of growth–climate relationships indicated that warming promoted open-grown tree growth across the entire altitude gradient, and also stand-grown tree growth at high elevations, but negatively affected the growth of stand-grown trees at low elevations. Water scarcity can be exacerbated by competition within forests, inhibiting the warming-induced benefits on tree growth. Moving window correlation analysis suggested the negative effect of warming on tree growth at low elevations was diminished after the late 1990s, as the drought stress was alleviated. Our research shows the divergent growth responses to warming of stand-grown and open-grown trees along elevation. It reveals effects of internal factors in determining tree growth response to warming and holds the potential to aid forest management and ecosystem models in responding to climate change. [ABSTRACT FROM AUTHOR]