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An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

Climate change is increasingly influencing tourism policy and practice and there is a growing need to assess climate risk for destinations and the potential implications for global tourism demand patterns. Climate-dependent tourism markets, such as beach tourism, are particularly sensitive to changes in climate, and understanding the future redistribution of tourism climate resources remains a gap in many world leading tourism regions. This paper presents the first climate change assessment of tourism climate resources in China. The Holiday Climate Index:beach (HCI:beach) and Holiday Climate Index:urban (HCI:urban) are calculated for 775 climate stations across China for the 1981–2010 baseline and mid and late-twenty-first century using projections from six CMIP5 Global Climate Models under low and high emission futures. The projected geographic and seasonal redistribution of tourism climate resources are advantageous for many climate-limited destinations but pose high heat risks for some major city destinations. The differential results for the HCI:beach and HCI:urban reinforce the importance of utilising market-specific indices to assess future climate risk. The results provide new decision-relevant climate information for tourism managers and destination planners throughout China. [ABSTRACT FROM AUTHOR]

Fatal landslides are a widespread geohazard that have affected millions of people and have claimed the lives of thousands around the globe. A change in climate has significantly increased the frequency and magnitude of rainfall, which affect the susceptibility of slopes to shallow landslides. This paper presents a methodological framework to assess the future changes in extreme and seasonal rainfall magnitudes with climate model projections. This framework was applied to project summer rainfall over Wanzhou County, China, using an ensemble of four regional climate models (RCMs) from the East Asian domain of the Coordinated Downscaling Experiment (CORDEX) under the Phase 5 Coupled Intercomparison Modeling Project (CMIP5). The results find that extreme daily rainfall was projected to decrease in the mid-21st century, with an uncertainty measured by a coefficient of variation between 5% and 25%. The mean seasonal rainfall is projected to increase in the mid-21st century up to a factor of 1.4, and up to a factor of 1.8 in the late-21st century. The variation in the mid-21st century ranged from 10% to 35%, and from 30% to 50% in the late-21st century. This case study delivered a proof-of-concept for a methodological framework to derive shallow landslide-triggering rainfall scenarios under climate change conditions. The resulting spatially distributed climate change factors (CCFs) can be used to incorporate future rainfall scenarios in slope susceptibility models and climate impact assessments. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]

Global climate change is expected to have a major impact on the hydrological cycle. Understanding potential changes in future extreme precipitation is important to the planning of industrial and agricultural water use, flood control, and ecological environment protection. In this paper, we study the statistical distribution of extreme precipitation based on historical observation and various global climate models (GCMs), and predict the expected change and the associated uncertainty. The empirical frequency, generalized extreme value (GEV) distribution, and L-moment estimator algorithms are used to establish the statistical distribution relationships and the multi-model ensemble predictions are established by the Bayesian model averaging (BMA) method. This ensemble forecast takes advantage of multi-model synthesis, which is an effective measure to reduce the uncertainty of model selection in extreme precipitation forecasting. We have analyzed the relationships among extreme precipitation, return period, and precipitation durations for 6 representative cities in China. More significantly, the approach allows for establishing the uncertainty of extreme precipitation predictions. The empirical frequency from the historical data is all within the 90% confidence interval of the BMA ensemble. For the future predictions, the extreme precipitation intensities of various durations tend to become larger compared to the historic results. The extreme precipitation under the RCP8.5 scenario is greater than that under the RCP2.6 scenario. The developed approach not only effectively gives the extreme precipitation predictions, but also can be used to any other extreme hydrological events in future climate. [ABSTRACT FROM AUTHOR]

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985–2014 relative to 1955–1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi‐model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30‐yr period is projected to increase for 2070–2099 relative to 1985–2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively. Inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 and especially high for the Amazon and the Tibetan Plateau. Plain Language Summary: Compound climate extremes, such as the events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. Can climate model simulate the historical compound extremes? If yes, how the global compound extremes will change in the future? In this study, we found that the global climate model performs well in simulating temporal changes of warm/wet and warm/dry extremes during the period 1955–2014. With greenhouse gas emissions continuing to increase in the future, compound warm/dry and warm/wet extremes show a continuous increase in frequency in the next few decades, while compound cold/dry and cold/wet extremes are projected to occur less frequently. Key Points: A multi‐model ensemble of CMIP6 models performs well in simulating temporal changes of warm/wet extremesThe inter‐model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremesThe projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070–2099 [ABSTRACT FROM AUTHOR]

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

The Holocene Temperature Conundrum refers to the mismatch between proxy-based temperature records and those based on climate model simulations. A possible reason for this mismatch is a putative proxy-based bias in reconstructed summer temperatures, and therefore, regional reconstructions of seasonal temperature are crucial for resolving the conundrum. In this paper, we reconstruct vegetation and climate changes over the last ∼10,000 years BP based on a high-resolution pollen record from Gushantun peatland, Changbai Mountains, Northeast China. Multiple quantitative reconstruction approaches were used and weighted averaging partial least squares regression (WAPLS) was found to be the most appropriate method for reconstructing Holocene temperature and precipitation. The reconstructed climate record shows that the Holocene Climate Optimum occurred between 8 ka and 6 ka and exhibited a cold month mean temperature that was 3 °C warmer than modern temperatures. Climate gradually cooled during late Holocene with a minimum cold month temperature of −19.6 °C. Four prominent cold events occurred around 8.7 ka BP, 7.8 ka BP, 5.7 ka BP, and 2.5 ka BP with an amplitude variation up to 3 °C. The synthesized seasonal temperature time series and a comparison with other proxies show that the decreasing trend in mean annual temperature is not a seasonal bias caused by summer temperature change. This study provides evidence of a Holocene seasonal temperature change at a regional scale and insights for further understanding of the Holocene Temperature Conundrum. • Reconstructed seasonal temperature changes based on a new high-resolution fossil pollen record in the Changbai Mountains • Synthesized climatic series in Northeast China to investigate the characteristics of regional temperature changes and their driving factors • Reconstructed winter temperature as evidence to address the alleged seasonal bias for the Holocene temperature conundrum [ABSTRACT FROM AUTHOR]

The Nanjing University of Information Science and Technology Earth System Model version 3 (NESM v3) has been developed, aiming to provide a numerical modeling platform for cross-disciplinary Earth system studies, project future Earth climate and environment changes, and conduct subseasonal-to-seasonal prediction. While the previous model version NESM v1 simulates the internal modes of climate variability well, it has no vegetation dynamics and suffers considerable radiative energy imbalance at the top of the atmosphere and surface, resulting in large biases in the global mean surface air temperature, which limits its utility to simulate past and project future climate changes. The NESM v3 has upgraded atmospheric and land surface model components and improved physical parameterization and conservation of coupling variables. Here we describe the new version's basic features and how the major improvements were made. We demonstrate the v3 model's fidelity and suitability to address global climate variability and change issues. The 500-year preindustrial (PI) experiment shows negligible trends in the net heat flux at the top of atmosphere and the Earth surface. Consistently, the simulated global mean surface air temperature, land surface temperature, and sea surface temperature (SST) are all in a quasi-equilibrium state. The conservation of global water is demonstrated by the stable evolution of the global mean precipitation, sea surface salinity (SSS), and sea water salinity. The sea ice extents (SIEs), as a major indication of highlatitude climate, also maintain a balanced state. The simulated spatial patterns of the energy states, SST, precipitation, and SSS fields are realistic, but the model suffers from a cold bias in the North Atlantic, a warm bias in the Southern Ocean, and associated deficient Antarctic sea ice area, as well as a delicate sign of the double ITCZ syndrome. The estimated radiative forcing of quadrupling carbon dioxide is about 7.24Wm-2, yielding a climate sensitivity feedback parameter of -0:98Wm-2 K-1, and the equilibrium climate sensitivity is 3.69 K. The transient climate response from the 1%yr-1 CO2 (1pctCO2) increase experiment is 2.16 K. The model's performance on internal modes and responses to external forcing during the historical period will be documented in an accompanying paper. [ABSTRACT FROM AUTHOR]

Simple Summary: Carposina coreana Kim is the most serious pest of Cornus officinalis. In recent years, its damage to C. officinalis has become increasingly serious, causing enormous economic losses in China. Here, the maximum entropy (MaxEnt) model was used to predict the distribution of C. coreana under current climate scenarios and future climate scenarios in China with ArcGIS software. Suitable areas for C. coreana under the current climate scenarios were mainly distributed in central China, and the highly suitable areas were distributed in southern Shaanxi, southwestern Henan, and northwestern Hubei. Under future climate scenarios, the boundaries of the suitable areas for C. coreana tended to shift to northern China. Given the predictive results of this study, we can clearly see the future diffusion trend of C. coreana in China, which has important theoretical significance for the control of this pest in China. Carposina coreana is an important pest of Cornus officinalis, distributed in China, Korea, and Japan. In recent years, its damage to C. officinalis has become increasingly serious, causing enormous economic losses in China. This study and prediction of current and future suitable habitats for C. coreana in China can provide an important reference for the monitoring, early warning, prevention, and control of the pest. In this study, the potential distributions of C. coreana in China under current climate and future climate models were predicted using the maximum entropy (MaxEnt) model with ArcGIS software. The distribution point data of C. coreana were screened using the buffer screening method. Nineteen environmental variables were screened using the knife-cut method and variable correlation analysis. The parameters of the MaxEnt model were optimized using the kuenm package in R software. The MaxEnt model, combined with key environmental variables, was used to predict the distribution range of the suitable area for C. coreana under the current (1971–2000) and four future scenarios. The buffer screening method screened data from 41 distribution points that could be used for modeling. The main factors affecting the distribution of C. coreana were precipitation in the driest month (Bio14), precipitation in the warmest quarter (Bio18), precipitation in the coldest quarter (Bio19), the standard deviation of seasonal variation of temperature (Bio4), minimum temperature in the coldest month (Bio6), and average temperature in the coldest quarter (Bio11). The feature class (FC) after the kuenm package optimization was a Q-quadratic T-threshold combination, and the regularization multiplier (RM) was 0.8. The suitable areas for C. coreana under the current climate model were mainly distributed in central China, and the highly suitable areas were distributed in southern Shaanxi, southwestern Henan, and northwestern Hubei. The lowest temperature in the coldest month (Bio6), the average temperature in the coldest quarter (Bio11), and the precipitation in the warmest quarter (Bio18) all had good predictive ability. In future climate scenarios, the boundary of the suitable area for C. coreana in China is expected to shift northward, and thus, most of the future climate scenarios would shift northward. [ABSTRACT FROM AUTHOR]

The wet bulb temperature (Tw) has gained considerable attention as a crucial indicator of heat-related health risks. Here we report south-to-north spatially heterogeneous trends of Tw in China over 1979-2018. We find that actual water vapor pressure (Ea) changes play a dominant role in determining the different trend of Tw in southern and northern China, which is attributed to the faster warming of high-latitude regions of East Asia as a response to climate change. This warming effect regulates large-scale atmospheric features and leads to extended impacts of the South Asia high (SAH) and the western Pacific subtropical high (WPSH) over southern China and to suppressed moisture transport. Attribution analysis using climate model simulations confirms these findings. We further find that the entire eastern China, that accommodates 94% of the country's population, is likely to experience widespread and uniform elevated thermal stress the end of this century. Our findings highlight the necessity for development of adaptation measures in eastern China to avoid adverse impacts of heat stress, suggesting similar implications for other regions as well. Attributing spatially heterogeneous heat stress trends to water vapor pressure changes driven by climate change-induced rapid warming in high-latitudes of East Asia, the authors predict widespread and uniform future heat stress in eastern China. [ABSTRACT FROM AUTHOR]

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While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km2. However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake's range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species. [ABSTRACT FROM AUTHOR]

Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model's performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China. [ABSTRACT FROM AUTHOR]

We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China (NEC) by employing climate and crop models. Based on historical data, diurnal temperature change exhibited a distinct negative relationship with maize yield, whereas minimum temperature correlated positively to rice yield. Corresponding to the evaluated climate change derived from coupled climate models included in the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative Concentration Pathway 4.5 scenario (RCP4.5), the projected maize yield changes for three future periods [2010-39 (period 1), 2040-69 (period 2), and 2070-99 (period 3)] relative to the mean yield in the baseline period (1976-2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The uncertainties in the crop response are discussed by considering the uncertainties obtained from both the climate and the crop models. The range of impact of the uncertainty became markedly wider when integrating these two sources of uncertainty. The probabilistic assessments of the evaluated change showed maize yield to be relatively stable from period 1 to period 3, while the rice yield showed an increasing trend over time. The results presented in this paper suggest a tendency of the yields of maize and rice in NEC to increase (but with great uncertainty) against the background of global warming, which may offer some valuable guidance to government policymakers. [ABSTRACT FROM AUTHOR]

This paper provides a review of paleoclimate modeling activities in China. Rather than attempt to cover all topics, we have chosen a few climatic intervals and events judged to be particularly informative to the international community. In historical climate simulations, changes in solar radiation and volcanic activity explain most parts of reconstructions over the last millennium prior to the industrial era, while atmospheric greenhouse gas concentrations play the most important role in the 20th century warming over China. There is a considerable model-data mismatch in the annual and boreal winter temperature change over China during the mid-Holocene [6000 years before present (ka BP)], while coupled models with an interactive ocean generally perform better than atmospheric models. For the Last Glacial Maximum (21 ka BP), climate models successfully reproduce the surface cooling trend over China but fail to reproduce its magnitude, with a better performance for coupled models. At that time, reconstructed vegetation and western Pacific sea surface temperatures could have significantly affected the East Asian climate, and environmental conditions on the Qinghai-Tibetan Plateau were most likely very different to the present day. During the late Marine Isotope Stage 3 (30-40 ka BP), orbital forcing and Northern Hemisphere glaciation, as well as vegetation change in China, were likely responsible for East Asian climate change. On the tectonic scale, the Qinghai-Tibetan Plateau uplift, the Tethys Sea retreat, and the South China Sea expansion played important roles in the formation of the East Asian monsoon-dominant environment pattern during the late Cenozoic. [ABSTRACT FROM AUTHOR]

Associations between tropospheric ozone (O3) and climate variations have been extensively investigated worldwide. However, given the lack of historical O3 monitoring data, the knowledge gaps regarding the influences of climate variations on long-term O3 trends in China remain. The present study used a tropospheric O3 dataset from the summers of 1999 to 2017 simulated by an atmospheric chemistry model to explore the linkage between summer O3 and a dominant atmospheric circulation system – the Western Pacific Subtropical High (WPSH) pressure – on an interannual basis in China. During this period, both WPSH strength and O3 concentrations in eastern and central China illustrated a growing trend. An EOF analysis was conducted to examine significant summer O3 characteristics and patterns and their potential connections with the WPSH. We find that the correlation between the first principal component of summer ozone concentration in the EOF analysis and the WPSH reached 0.56 (P≤0.01) in China from 1999 to 2017. We show that the WPSH determines interannual fluctuations of summer O3 , whereas O3 precursor emissions contribute primarily to the O3 long-term trend. Our results reveal that the WPSH plays a vital role in O3 perturbation in the eastern seaboard regions and inland China. Precursor emissions made more significant contributions of up to 60 % to increasing O3 trends in the inland urban agglomerations than coastal regions in eastern and southern China. The strongest contribution of meteorological conditions associated with the WPSH to summer O3 occurred in the Yangtze River Delta (YRD), accounting for over 9 % to ozone perturbations from 1999 to 2017. We find that the effect of the WPSH on regional O3 depends on the spatial proximity to the WPSH. We attributed the effects of the WPSH on O3 interannual variations to the changes in air temperature, precipitation, and winds associated with the WPSH's intensity and positions. [ABSTRACT FROM AUTHOR]

This paper examines variations of the surface air temperature (SAT) over China and the globe in the twentieth century simulated by 19 coupled climate models driven by historical natural and anthropogenic forcings. Most models perform well in simulating both the global and the Northern Hemispheric mean SAT evolutions of the twentieth century. The inclusion of natural forcings improves the simulation, in particular for the first half of the century. The reproducibility of the SAT averaged over China is lower than that of the global and hemispheric averages, but it is still acceptable. The contribution of natural forcings to the SAT over China in the first half of the century is not as robust as that to the global and hemispheric averages. No model could successfully produce the reconstructed warming over China in the 1920s. The prescribed natural and anthropogenic forcings in the coupled climate models mainly produce the warming trends and the decadal- to interdecadal-scale SAT variations with poor performances at shorter time scales. The prominent warming trend in the last half of the century over China and its acceleration in recent decades are weakly simulated. There are discrepancies between the simulated and observed regional features of the SAT trend over China. Few models could produce the summertime cooling over the middle part of eastern China (27°–36°N), while two models acceptably produce the meridional gradients of the wintertime warming trends, with north China experiencing larger warming. Limitations of the current state-of-the-art coupled climate models in simulating spatial patterns of the twentieth-century SAT over China cast a shadow upon their capability toward projecting credible geographical distributions of future climate change through Intergovernmental Panel on Climate Change (IPCC) scenario simulations. [ABSTRACT FROM AUTHOR]

Linking sinuosity, a fairly recently developed metric, with high temperature extremes (HTEs) can be both useful and insightful to better understand the physical mechanisms behind HTEs. However, it is not clear whether there exists a relationship between the sinuosity changes and HTE changes in present and future climate conditions over southeastern China. In this paper, the anomalous characteristics of the atmospheric circulation are quantified by sinuosity. Three sinuosity metrics are used in this study: individual sinuosity (SIN), aggregate sinuosity (ASIN), and comprehensive sinuosity (CSIN). Furthermore, we examine the relationship between sinuosity changes and HTE changes in present and future climate conditions. ASIN is strongly correlated with surface air temperature (SAT). We find that the influence of individual sinuosity (SIN) at different latitudes on the SAT of southeastern China is different. The SIN of low (middle) latitude isohypses has significant positive (negative) correlations with the SAT of southeastern China. The SIN of high-latitude isohypses is rather limited and can therefore be ignored. The projected relationship between the sinuosity changes and HTE changes in the late 21st century suggests similar results. The change in SAT is related to the changes in climate variables over southeastern China in the future, and these changes increase with the increase in Z500 or V850 and the decrease in U500. Moreover, the frequencies of large (small) comprehensive sinuosity (CSIN) values at low (mid) latitudes will increase. At the end of the 21st century, Z500 isohypses at different latitudes will have an obvious poleward shift. Our results indicate that measuring the aggregate waviness of the midtropospheric flow (via sinuosity) can provide insight regarding HTEs, and the climate model output can be used to examine the future likelihood of increased HTE. [ABSTRACT FROM AUTHOR]

Associations between tropospheric ozone (O3) and climate variations have been extensively investigated worldwide. However, given the lack of historical O3 monitoring data, the knowledge gaps regarding the influences of climate variations on long-term O3 trends in China remain. The present study used a unique tropospheric O3 dataset from the summer of 1999 to 2017 simulated by an atmospheric chemistry model to explore the linkage between summer O3 and a dominant atmospheric circulation system – the Western Pacific Subtropical High Pressure (WPSH) on an interannual basis in China. During this period, both WPSH strength and O3 concentrations in eastern and central China illustrated a growing trend. An EOF analysis was conducted to examine significant summer O3 characteristics and patterns and their potential connections with the WPSH. We show that the WPSH determines interannual fluctuations of summer O3, whereas O3 precursor emissions contribute primarily to the O3 long-term trend. Special efforts were made to discern the associations of O3 variations in major urban agglomerations of China and the WPSH. The results reveal that the WPSH plays a more vital role in O3 perturbation in the eastern seaboard regions and inland China, but leads to lower O3 levels in the Pearl River Delta (PRD) region. Precursor emissions made more significant contributions up to 60 % to increasing O3 trends in the inland urban agglomerations than coastal regions in eastern and southern China. The strongest contribution of meteorological conditions associated with the WPSH to summer ozone concentration occurred in the Yangtze River Delta (YRD), accounting for over 9 % to ozone perturbations from 1999 to 2017. Overall, we find that the effect of the WPSH on regional O3 depends on the spatial proximity to the WPSH. We attributed the effects of the WPSH on O3 interannual variations to the changes in air temperature, precipitation, and winds associated with the WPSH's intensity and positions. [ABSTRACT FROM AUTHOR]

High-impact weather affects the safety and economic operation of power systems. In this study, to provide regional microclimate of high-impact weather for the local power grid system in the northern Heibei province (known as the Jibei region in China), ERA5-Land global reanalysis data during 1981–2020 with a 0.1° grid size (about 9 km) are adopted to analyze the climate statistics and changes of the disastrous weather that affects the power grids. The results show that there have been significant climate changes in the region, including a temperature increase of about 1 °C, evident humidity and precipitation reductions, for the Jibei region and the six sub-regions that concentrated with wind and solar energy development in the 40 years. Due to the differences in terrain, the climate changes differ significantly among the six renewable energy development regions. The main types of high-impact weather that affect the power grid in the region are heavy fog and icing events, followed by cold waves, snowstorms, and rainstorms. In general, with climate changes in the last several decades, the weather disasters in Jibei region have become more frequent. Since most high-impact weather events have a small scale, it is necessary to simulate the weather processes with high-resolution models to accurately quantify the characteristics of the weather processes that affect the power grid. Therefore, a refined regional meteorological model (with grid size of 2 km) based on four-dimensional data assimilation (JB-FDDA) is established for the Jibei region. With one year of model reanalysis data, we found that JB-FDDA can significantly improve the accuracy of the local meteorological fields, and properly depicted the details of severe weather that affect the power grid operation. This study provide an analytical approach for regional electricity meteorological disasters by using reanalysis data. [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]

Drought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously. [ABSTRACT FROM AUTHOR]

Human influence on regional warming since 1901 has received little attention because of limited data during the early period. This study investigates the relative contribution of different external forcings to observed annual, summer, and winter warming in China over the period 1901–2018. First, four observational datasets were compared to validate data representativeness, particularly during the early twentieth century. Observed temperature changes were then compared with outputs from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) based on an optimal fingerprinting method. Generally, both generations of climate models were able to reliably reproduce long-term warming in China over the period 1901–2018; however, they slightly underestimate the amplitude of annual and winter temperature increases. The observed annual warming of 1.54°C from 1901 to 2018 was more rapid than the global mean and was mostly attributable to the anthropogenic forcing signal. The three-signal detection analyses, including greenhouse gas (GHG), anthropogenic aerosol (AA), and natural external (NAT) forcings, indicated the detectable and distinct influence of GHG and AA signals on annual, summer, and winter temperatures during 1901–2018. For annual mean temperature, the GHG and AA contributed to 2.06°C (from 1.58° to 2.54°C) and −0.45°C (from −0.17° to −0.73°C) of observed change, respectively. The GHG signal was detectable from individual CMIP6 models and thus was indicative of the robustness of this influence. While during 1951–2018, GHG and AA were simultaneously detected in the summer temperatures based on the CMIP6 models; here, the AA cooling effects offset approximately 25% of GHG-induced warming. [ABSTRACT FROM AUTHOR]

Global climate change is projected to increase the probability of occurrence and severity of droughts. Increased CO2 concentration drives partial closure of plant stomata and reduces evapotranspiration. However, the impact of reduced evapotranspiration due to CO2 on future droughts characteristics in China is unclear. In this study, we have used the Coupled Model Intercomparison Project Phase 6 (CMIP6) global climate model simulations (GCMs) under historical period, and four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5) to estimate drought. We used a widely applicable drought index (i.e., the self‐calibrating Palmer Drought Severity Index [scPDSI]) to evaluate the historical and future drought conditions, using the Penman–Monteith formula with and without the CO2 concentrations. The results show that the increasing trend of scPDSI slows down when the CO2 effect is considered in all scenarios, especially in Heilongjiang, northern Xinjiang, and the Qinghai–Tibet Plateau. The divergence in the slowdown trend among the different scenarios becomes greater after 2030, with higher emission scenarios implying a greater slowdown in the increasing trend of scPDSI. After considering CO2, drought characteristics such as the drought frequency, intensity, and drought area decrease. Increased CO2 concentration on vegetation physiological processes could mitigate future drought. Therefore, the effects of CO2 on plant physiology should be considered in studies of future drought trends to develop more realistic response and adaptation policies to future drought changes. [ABSTRACT FROM AUTHOR]

The source region of the Yangtze River (SRYR) is located in the hinterland of the Tibetan Plateau (TP). Due to the harsh climate environment, there are few meteorological stations in its territory, and the measured meteorological data are also less. To overcome the impact of lack of measured meteorological data, the China Meteorological Forcing Dataset (CMFD) was used to couple with the meteorological data, to make the meteorological data more representative of the average climate state of the SRYR. Furthermore, we used the Soil and Water Assessment Tool (SWAT) to verify interpolation effect of meteorological data. Then the six global climate models (GCMs) were integrated through deep learning (DL) algorithm to make it closer to the changes in weather data after correction. The future water resource changes of SRYR were obtained by coupling the SWAT model with the integrated meteorological data. The results show that the CMFD dataset has a high precision in the SRYR, and that the average relative error (RE), coefficient of determination (R2), Nash–Sutcliffe efficiency (Ens) with the measured temperature and precipitation are 10.66%, 0.99, and 0.93, respectively. The CMFD dataset can be used for meteorological data integration. After the meteorological data integration, the Ens between simulated runoff and observed runoff increased from 0.64 to 0.70, and the RE reduced from 13.4 to 10.3%. Under the future climate scenario, the runoff in the SRYR shows a decreasing trend, the average annual runoff decreased by 11.89%, and the distribution of runoff during the year changes is even more dramatic. This shows that the SRYR faces the risk of water resources reduction, which will bring challenges to water resources management in the SRYR. This study provides a new idea for meteorological data correction. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) is an integral part of the regional hydrological cycle and energy balance and is extremely sensitive to climate change. Based on temperature data from 24 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study developed a multi-model ensemble based on delta statistical downscaling with multiple interpolation methods and evaluation indicators to predict the spatial and temporal evolution trends of ET0 in the Yellow River Basin (YRB) under four emission scenarios (SSP126, SSP245, SSP370, and SSP585) for the near- (2022–2040), mid- (2041–2060), and long- (2081–2100) term future. Results demonstrate that regional data generated based on delta statistical downscaling had good simulation performance for the monthly mean, maximum, and minimum temperatures in the YRB, and the developed multi-model ensemble had better simulation capability than any single model. Compared to the historical period (1901–2014), the annual ET0 showed a highly significant increase for different future emission scenarios, and the increase is faster with increasing radiative forcing. The first main cycle of ET0 change was 52, 53, 60, and 48 years for the SSP126, SSP245, SSP370, and SSP585, respectively. ET0 in the YRB had positive values for EOF1 under all four emission scenarios, responding to a spatially consistent trend across the region. Compared to the historical period, the spatial distribution of ET0 under different future emission scenarios was characterized by being larger in the west and smaller in the east. As the radiative forcing scenario increased and time extended, ET0 significantly increased, with a maximum variation of 112.91% occurring in the western part of the YRB in the long-term future under the SSP585 scenario. This study can provide insight into the water cycle patterns of watersheds and scientific decision support for relevant departments to address the challenges of climate change. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) as measured by the difference between daily maximum (Tmax) and minimum (Tmin) temperatures is of great importance to human health, ecology, and agriculture. The link of its long-term change to anthropogenic forcing is still unclear. This study shows evidence of human influence on long-term changes in DTR over the globe, five continents, and China during the past century (1901–2014). Using multiple observational datasets, we find a general decrease in the DTR over most of the global land since 1901, especially after the mid-1950s. Changes in DTR are due to different warming rates of Tmax and Tmin in response to external forcings. The climate models that participated in phase 6 of the Coupled Model Intercomparison Project Phase 6 (CMIP6) generally reproduce most of the changes in DTR, along with those in Tmax and Tmin. The models have underestimated the observed changes in DTR, however. A formal detection and attribution analysis shows that the anthropogenic forcing signal, including both greenhouse gas and aerosol emissions but dominated by the greenhouse gas emissions, is the main driver for these changes. The anthropogenic aerosol signal can be detected in Tmax and Tmin but not in DTR during the period of 1901–2014 over the globe and most continents. These indicate the observed decrease in DTR is not a simple response to anthropogenic aerosol emission. The natural signal is negligible in almost all the cases. Globally, anthropogenic influence is estimated to explain more than 90% of the observed changes in the three variables. In China, human influence is also clearly detected, although model simulated results on the regional scale have larger deviation. Significance Statement: The diurnal temperature range (DTR) is of great importance in many areas. We compare multiple observational datasets with the simulations by climate models that participated in the latest phase (phase 6) of the Coupled Model Intercomparison Project (CMIP6), finding evidence of human influence on long-term changes in DTR over the past century (1901–2014) and robust evidence for the period since the early 1950s. The decrease in DTR as seen in the observational dataset is caused by different warming rates of daily maximum and daily minimum temperature in response to anthropogenic forcing, including both greenhouse gases and aerosols. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the uncertainty ranges of high flows under climate change in Jinhua River basin, eastern China. Four representative concentration pathways (RCPs), three global climate models (GCMs), 10 downscaling parameter sets and three hydrologic models are applied to simulate future discharges. Changes of annual maximum discharges are assessed for the baseline period (1961-1990) and future period (2011-2040). The uncertainties of annual maximum discharges are calculated for each uncertainty source and compared with different combinations of them. The minimum temperature will probably increase all year round in the future period and maximum temperature would increase in most cases. The changes of precipitation showed different directions by different models and emission scenarios. The annual maximum discharges would decrease for all RCPs. The order of uncertainty ranges of high flows due to different uncertainty sources from high to low is: hydrologic models, GCMs, parameter sets in the downscaling method and emission scenarios. It must be noted that the small uncertainty contribution from different emission scenarios is due to the study period when the differences in increase of radiative forcing and greenhouse gas concentration are less obvious between different RCPs compared to the second half of the 21st century. [ABSTRACT FROM AUTHOR]

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade. [ABSTRACT FROM AUTHOR]

Global warming has altered the thermodynamic and dynamic environments of the climate system, thus affecting the energy budget and water cycle process of the land-atmosphere system. Under changes in key hydrological elements such as precipitation, runoff, and terrestrial water storage, future drought variation remains a complex question. Existing studies have utilized terrestrial water storage anomaly (TWSA) in drought monitoring and assessment, but they usually focused on either drought duration or intensity, overlooking the multi-faced attributes of droughts as well as their socioeconomic impacts under a non-stationary condition. In this study, we first identify dry/wet conditions over China using GRACE/GRACE-FO satellite observations, and then evaluate the feedback effects of humidity and energy factors (e.g., sensible heat flux, latent heat flux, atmospheric relative humidity, and convective available potential energy) to drought events. Future changes in TWSA and dry/wet conditions are projected by eight Coupled Model Inter-comparison Project Phase 6 (CMIP6) global climate models (GCMs) under three shared socioeconomic pathways (SSPs), with their biases corrected by a trend-preserving quantile mapping method. The time-varying Copula function of drought duration and intensity is constructed by a moving windows method, and future bivariate drought risks are quantified with the most likely realization method. The population and GDP affected by increasing drought risks are finally quantified based on the SSPs data. It is found that the land-atmosphere coupling effects closely interact with drought evolution, and the uneven distribution of water resources is projected to be further aggravated, with most areas of China will be threatened by continuous drying tendency. By the end of the century, the duration of moderate, severe and exceptional droughts in some regions of China will double, and the drought intensity will increase by over 80%. For the 50-year bivariate droughts during the historical period, their occurrence may increase by 5–10 times in several regions, and might affect about 35–55% of China's population and GDP at the end of 21st century. [ABSTRACT FROM AUTHOR]

In recent years, China has suffered from frequent extreme precipitation events, and predicting their future trends has become an essential part of the current research on this issue. Because of the inevitable uncertainties associated with individual models for climate prediction, this study uses a machine learning approach to integrate and fit multiple models. The results show that the use of several evaluation metrics provides better results than the traditional ensemble median method. The correlation coefficients with the actual observations were found to improve from about 0.8 to 0.9, while the correlation coefficients of the precipitation amount (PRCPTOT), very heavy precipitation days (R20mm), and extreme precipitation intensity (SDII95) reached 0.95. Based on this, the precipitation simulations of moderate forced scenario for sharing socio-economic path (SSP2-4.5) from 27 coupled models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to explore potential changes in future extreme precipitation events in China and to calculate the distribution and trends of the PRCPTOT, extreme precipitation amount (R95pTOT), maximum consecutive 5-day precipitation (Rx5day), precipitation intensity (SDII), SDII95, and R20mm for the early 21st century (2023–2050), mid-21st century (2051–2075), and late 21st century (2076–2100), respectively. The results showed that the most significant increase in extreme precipitation indices is expected to occur by the end of the century, with the R95pTOT, Rx5day, and SDII95 increasing by 13.73%, 9.43%, and 9.34%, respectively, from the base period. The remaining three precipitation indexes, the PRCPTOT, SDII, and R20mm, also showed increases of 8.77%, 6.84%, and 4.02%, respectively. Additionally, there were apparent differences in the spatial variation of extreme precipitation. There were significant increasing trends of extreme precipitation indexes in central China and northeast China in the three periods, among which the total annual precipitation showed an increasing trend in central and northern China and a decreasing trend in western and south China. An increasing trend of annual precipitation intensity was found to be mainly concentrated in central China and south China, and the annual precipitation frequency showed a larger increasing trend at the beginning of this century. The annual precipitation frequency showed an increasing trend in the early part of this century. In general, all the indices showed an overall increasing trend in the future period, with the PRCPTOT, Rx5day, and SDII95 showing the most significant overall increasing trends. [ABSTRACT FROM AUTHOR]

Vegetation activity and phenology are significantly affected by climate change, and changes in vegetation activity and phenology can in turn affect regional or global climate patterns. As one of the world’s great biomes, temperate grasslands have undergone remarkable changes in recent decades, but the connections between vegetation activity and phenology changes and regional climate there have remained unclear. Using the observation minus reanalysis (OMR) method, this study investigated the possible effects of vegetation activity and vegetation growing season changes on air temperatures in temperate grasslands of China. The results showed that average NDVI of the temperate grassland significantly increased by 0.011 decade21 for the growing season during 1982–2015. The growing season started earlier and ended later, resulting in an extension. Increased vegetation activity during spring and autumn significantly warmed spring and autumn air temperatures by reducing albedo. By contrast, summer greening had no significant effect on summer temperature, due to the opposing effects of decreased albedo and enhanced evapotranspiration on temperature. The earlier start and later end of the growing season contributed to warmer spring and autumn air temperatures. As phenological changes had no significant effect on summer temperature, the extended growing season warmed air temperature. Our results suggest that the climate change–induced increasing vegetation activity and extended growing seasons can further aggravate regional warming in temperate grasslands of China, implying that the effects of vegetation activity and phenology changes on regional climate should be considered in climate models for accurately simulating climate change in temperate grasslands. [ABSTRACT FROM AUTHOR]

The goal of carbon neutrality provides a new opportunity for the development of renewable energy, especially for wind power. In this paper, 11 regional climate models (RCMs) including CORDEX-EA and PRECIS at two different resolutions are used to evaluate the performance in simulating the spatio-temporal characteristics for wind speed and energy potential from 1981 to 2005. The results show that most models can reproduce the spatio-temporal patterns, and the simulation of the eastern region is better than that of the western region. Besides, most of the higher-resolution models had better simulation performance for spatial-temporal patterns and inter-annual variation. Then, the weighted multi-model ensemble is used to project the changes of the wind speed and the wind power density over China's mainland during the mid-21st century under the RCP8.5 scenario. In future, a decrease in wind speed is projected by most RCMs over China, although some regions, such as the southern parts of China, are projected to have more wind energy potential. The percentage variation of annual mean wind speed will remain in the range of ±4%. The wind power density will decrease in the north and the largest decrease will be found in the northwest. However, the annual mean wind power density in the southeast China will have an increase of 2.21% in the middle of this century. The inter-annual variation of the wind power density in most regions will increase by more than 20%. However, intra-annual variation in the wind power density is likely to decrease in most regions of China, ranging from about −20% to −50%. • A weighted multi-model ensemble was used to project the changes of wind power density over China. • The changes of annual mean wind power density vary in different sub-regions of China, ranging from −3.21% to 2.21%. • The inter-annual variation of the WPD in most regions will increase by more than 20%. [ABSTRACT FROM AUTHOR]

The Yellow River Basin is an important economic belt and key ecological reservation area in China. In the context of global warming, it is of great significance to project the drought disaster risk for ensuring water security and improving water resources management measures in practice. Based on the five Global Climate Models (GCMs) projections under three scenarios of the Shared Socioeconomic Pathways (SSP) (SSP126, SSP245, SSP585) released in the Sixth Coupled Model Intercomparison Project (CMIP6), this study analyzed the characteristics of meteorological drought in the Yellow River Basin in combination with SPEI indicators over 2015–2100. The result indicated that: (1) The GCMs from CMIP6 after bias correction performed better in reproducing the spatial and temporal variation of precipitation. The precipitation in the Yellow River Basin may exhibit increase trends from 2015 to 2100, especially under the SSP585 scenario. (2) The characteristics of meteorological drought in the Yellow River Basin varied from different combination scenarios. Under the SSP126 scenario, the meteorological drought will gradually intensify from 2040 to 2099, while the drought intensity under SSP245 and SSP585 scenarios will likely be higher than SSP126. (3) The spatial variation of meteorological drought in the Yellow River Basin is heterogeneous and uncertain in different combination scenarios and periods. The drought tendency in the Loess Plateau will increase significantly in the future, and the drought frequency and duration in the main water conservation areas of the Yellow River Basin was projected to increase. [ABSTRACT FROM AUTHOR]

The South-to-North Water Diversion Project (SNWDP) is a national strategic project for water shortages in northern China. Climate change will affect the availability of water resources in both source and receiving areas. A grid-based RCCC-WBM model based on climate projections from nine Global Climate Models under SSP2-4.5 was used for analyzing the changes in temperature, precipitation, and streamflow in the near future (2025–2045, NF) and far future (2040–2060, FF) relative to the baseline (1956–2000). The results showed that: (1) the temperature of the western route will increase significantly in the NF and FF with an extent of 1.6 °C and 2.0 °C, respectively, (2) precipitation will very likely increase even though Global Climate Model (GCM) projections are quite dispersed and uncertain, and (3) over half of the GCMs projected that streamflow of receiving area will slightly increase with a rate of 1.68% [−8.67%, 12.3%] and 2.78% [−3.30%, 11.0%] in the NF and FF, respectively. Climate change will support the planning of the western route to a certain extent. However, water supply risk induced by the extreme situation of climate change should be paid adequate consideration when the project operates in practice due to the large dispersion and uncertainty of GCM projections. [ABSTRACT FROM AUTHOR]

To support future solar energy deployment in China, long‐term changes in solar energy resources over China were investigated based on high‐resolution dynamical downscaling simulations under three emission scenarios. First, an evaluation of model performance was conducted through comparison with station and ERA5 data, which indicated that although consistent overestimation was found, the spatial distribution and intra‐annual variation of solar radiation were captured well by ensemble simulation, showing added value over regions with complex topography as well. Then, the trends of the solar power output from photovoltaic (PV) systems during 2020–2099 were projected, characterized by an increase in east and central China, and a consistent decrease in the solar‐energy‐abundant regions (e.g., northeast China, the Tibetan Plateau, and northwest China) under the three scenarios. Except for under the low emission scenario, the regional averaged trends of PV were projected to decrease consistently over China in all four seasons, with significant magnitude apparent in both spring and winter. In addition, a more intense magnitude of decline was found under the higher emission scenario, with more subregions with a significant trend and higher intra‐annual and inter‐annual variability. However, it seems that the emission scenarios impact the spatial pattern only remarkably in the late century, however strengthened the decreasing magnitude in all future periods. In addition, results indicate decline in the future PV potential over most of subregions of China due to reduced radiation and increased temperature, but for the decrease over Tibetan Plateau the reduced wind speed also act as an important factor. Plain Language Summary: The long‐term goal of carbon neutrality in China involves new requirements for the development and utilization of solar energy. The present study was motivated by supporting future solar energy deployment at local scales. However, due to data limitations, relatively little effort has been made to project long‐term changes in solar energy over different subregions of China. Accordingly, we investigated this based on a set of high‐resolution dynamical downscaling simulations under different climate scenarios, and found that the higher emission scenarios reinforce the magnitude of the decline in solar energy and result in higher inter‐annual variability. These findings suggest an increased need for constraining emissions for the benefit of solar energy. In addition, we found that radiation will likely be the most important factor related to future changes in solar resources over most of subregions under RCP2.6 scenario, however the effect of wind speed should not be ignored over Tibetan Plateau. Key Points: Despite some overestimation, the spatial distribution and intra‐annual variation of solar radiation were captured well, showing added valuesFuture solar power were projected to generally increase in east and central China but decrease in solar‐energy‐abundant regionsRadiation was the most robust factor for future solar energy trend over China, however wind speed can not be ignored over Tibetan Plateau [ABSTRACT FROM AUTHOR]

Normandy is the world's leading producer of flax fiber (Linum usitatissimu L.), which is mainly exported to China for textile manufacturing. Flax is a plant that is cultivated in spring and that grows in an oceanic climate with regular watering and limited thermal excesses. This article aims at projecting the impact of climate change on the phenology of this plant when subjected to climatic hazards that may occur during its development. These projections use two regional climate models (ALADIN-Climate and WRF) based on the two scenarios generated by the latest IPPC report—intergovernmental panel on climate change—(RCP 4.5 and RCP 8.5). The rise in temperatures would result in a time-cycle reduction. Consequently, flax would not be exposed to the early summer water shortage. However, thermal conditions could be unfavorable, especially due to the increased frequency of heat days. Flax is also exposed to the risk of lodging during heavy rainfall episodes; however, the results are somehow contrasted between the two climate models used. This research demonstrates the interest of multidisciplinary impact studies so as to anticipate the consequences of climate change on agricultural crops. [ABSTRACT FROM AUTHOR]

Under the influence of climate change, the hydrological processes of glaciers have undergone significant changes, a fact which is seriously affecting agricultural production in the downstream region of the Tianshan Mountains, China. In order to explore the intrinsic relationship between climate change and hydrological elements, we proposed an "evaluation-driving-prediction" system to study it. First, we constructed a glacier-enhanced soil and water assessment tool model (GE-SWAT) and used a two-stage calibration method to optimize the model parameters. Next, a scenario analysis was used to evaluate the driving factors of historical runoff changes. Finally, we projected future runoff changes using bias-corrected regional climate model (RCM) outputs. The results of the case study on the Jinghe River Basin in the Tianshan Mountains show that from 1963 to 2016, total runoff increased by 13.3%, 17.7% of which was due to increasing precipitation and 1.8% of which was negated by rising temperatures. The glacier runoff increased by 14.5%, mainly due to the rising temperatures. A 3.4% reduction in snowmelt was caused by a lower snowfall/precipitation ratio, which significantly reduced the snowfall from June to August. The RCM projection indicated that the warming and humidification phenomenon in the study area will continue at least through to the mid-21st century. A consistent increase in glacier runoff and total runoff is projected, but the contribution rate of the glacier runoff will have little to no change under the RCP4.5 and RCP8.5 emission scenarios. Our research demonstrates the simulation performance of the GE-SWAT model in a basin with moderate glacier cover. This method is shown to be efficient in quantifying the impact of climate change on glacier hydrological processes and predicting future streamflow changes, providing a good research reference for similar regions. [ABSTRACT FROM AUTHOR]

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

Vegetation and land-surface hydrology are intrinsically linked under long-term climate change. This paper aims to evaluate the dynamics of potential natural vegetation arising from 21st century climate change and its possible impact on the water budget of the Hanjiang River basin in China. Based on predictions of the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC-SRES) A1 scenario from the PRECIS (Providing Regional Climates for Impact Studies) regional climate model, changes in plant functional types (PFTs) and leaf area index (LAI) were simulated via the Lund-Potsdam-Jena dynamic global vegetation model. Subsequently, predicted PFTs and LAIs were employed in the Xinanjiang vegetation-hydrology model for rainfall-runoff simulations. Results reveal that future long-term changes in precipitation, air temperature and atmospheric CO2 concentration would remarkably affect the spatiotemporal distribution of PFTS and LAIS. These climate-driven vegetation changes would further influence regional water balance. With the decrease in forest cover in the 21st century, plant transpiration and evaporative loss of intercepted canopy water will tend to fall while soil evaporation may rise considerably. As a result, total evapotranspiration may increase moderately with a slight increase in annual runoff depth. This indicates that, for long-term hydrological prediction, climate-induced changes in terrestrial vegetation cannot be neglected as the terrestrial biosphere plays an important role in land-surface hydrological responses. [ABSTRACT FROM AUTHOR]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline 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). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]

Climate change affects many aspects of the physiological and biochemical processes of growing maize and ultimately its yield. A comprehensive climate suitability model is proposed that quantifies the effects of temperature, precipitation, solar radiation, and wind in different phenological stages of maize. It is calibrated using weather and yield data from China's Henan Province. The comprehensive suitability model showed the capability of correctly hindcasting observed temporal and spatial changes in maize phenology in response to climatic factors. The predicted yield based on the suitability model can well match the recorded field yield very well from 1971–2020. The results of correlation showed that the yields are more closely related to multi-weather factors, temperature and precipitation than to solar radiation and wind. The sensitivity analysis illustrates that temperature and precipitation are the dominant weather factors affecting yield changes based on a direct differentiation method. The comprehensive suitability model can provide a scientific support and analysis tool for predicting grain production considering climate changes. [ABSTRACT FROM AUTHOR]

Both climate and land-use changes can influence drought in different ways. Thus, to predict future drought conditions, hydrological simulations, as an ideal means, can be used to account for both projected climate change and projected land-use change. In this study, projected climate and land-use changes were integrated with the Soil and Water Assessment Tool (SWAT) model to estimate the combined impact of climate and land-use projections on hydrological droughts in the Lutheran River basin. We showed that the measured runoff and the remote sensing inversion of soil water content were simultaneously used to validate the model to ensure the reliability of the model parameters. Following calibration and validation, the SWAT model was forced with downscaled precipitation and temperature outputs from a suite of nine global climate models (GCMs) based on CMIP5, corresponding to three different representative concentration pathways (RCP2.6, RCP4.5 and RCP8.5) for three distinct time periods: 2011–2040, 2041–2070 and 2071–2100, referred to as early century, mid-century and late-century, respectively, and the land use predicted by the CA–Markov model in the same future periods. Hydrological droughts were quantified using the standardized runoff index (SRI). Compared to the baseline scenario (1961–1990), mild drought occurred more frequently during the next three periods (except for the 2080s under the RCP2.6 emissions scenario). Under the RCP8.5 emissions scenario, the probability of severe drought or above occurring in the 2080s increased, the duration was prolonged, and the severity increased. Under the RCP2.6 scenario, the upper central region of the Luanhe River in the 2020s and upper reaches of the Luanhe River in the 2080s were more likely to experience extreme drought events. Under the RCP8.5 scenario, the middle and lower Luanhe River in the 2080s was more likely to experience these conditions. [ABSTRACT FROM AUTHOR]

Understanding future drought risks plays a crucial role in developing climate change adaptation strategies and in enhancing disaster resilience. However, previous studies may lead to biased conclusions due to the neglect of two factors, including the relative performance of climate simulations and the uncertainty in drought characterization. In this study, Bayesian model averaging is used to merge five regional climate model simulations and to project future changes in hydroclimatic regimes over China under two representative emission scenarios (RCP4.5 and RCP8.5). Drought characteristics, including drought severity and duration, are extracted using the Standardized Precipitation Evapotranspiration Index (SPEI). A Bayesian copula approach is used to uncover underlying interactions of drought characteristics and associated uncertainties across 10 climate divisions of China. The regional return periods of drought characteristics are used to assess future changes in multidimensional drought risks and the probability of extreme droughts. Our findings reveal that the variations in drought characteristics are generally underestimated by the ensemble mean (AEM) simulation. The Bayesian framework improves the reliability and accuracy of hydroclimate simulations and better reproduces the drought regimes compared to the AEM simulation. The drought duration and severity are projected to substantially increase for most areas of China based on the Bayesian framework, but the AEM simulation may lead to multiple opposite behaviors, especially under RCP4.5. The estimated joint risk from drought duration and drought severity is expected to increase under both emission scenarios. The likelihood of extreme droughts is also projected to increase as the radiative forcing increases. [ABSTRACT FROM AUTHOR]

The planetary boundary layer (PBL) plays an essential role in climate and air quality simulations. Nevertheless, large uncertainties remain in understanding the drivers for long-termtrends of PBL height (PBLH) and its simulation. Herewe combine the radiosonde data and reanalysis datasets to analyze PBLHlong-termtrends over China, and to further explore the performance of CMIP6 climate models in simulating these trends. Results show that the observed long-term "positive-tonegative" trend shift of PBLH is related to the variation in the surface upward sensible heat flux (SHFLX), and the SHFLX is further controlled by the synergistic effect of low cloud cover (LCC) and soil moisture (SM) changes. Variabilities in LCC and SM directly influence the energy balance via surface net downward shortwave flux (SWF) and the latent heat flux (LHFLX), respectively. The CMIP6 climate models, however, cannot reproduce the observed PBLH long-term trend shift over China. The CMIP6 results illustrate an overwhelming continuous downward PBLH trend during the 1979-2014 period, which is largely caused by the poor capability in simulating long-term variations of cloud radiative effect. Our results reveal that the long-term cloud radiative effect simulation is critical for CMIP6 models in reproducing the long-term trend of PBLH. This study highlights the importance of processes associated with LCC and SM in modulating PBLH long-term variations and calls attention to improve these processes in climate models in order to improve the PBLH long-term trend simulations. SIGNIFICANCE STATEMENT: Investigating the long-term variations of the planetary boundary layer height (PBLH) is of great importance in understanding the complicated interactions between climate change and the planetary boundary layer. The availability of long-term radiosonde observations, along with reanalysis data and data from phase 6 of the Coupled Model Intercomparison Project, provides an excellent opportunity for us to analyze the long-term variability of PBLH and explore the capability of global climate models in reproducing the observed trend of PBLH. We argue that variations of cloud cover and soil moisture dominate the long-term variability of PBLH over China, given their key role in modifying the surface energy partition and balance. More importantly, whether cloud processes can be well simulated or not is key to reproducing the observed PBLH variations in global climate models, which merits further process-based observation in the future. [ABSTRACT FROM AUTHOR]

Climate change has affected the dynamics of the water cycle and its consequent effects on water resources in many regions. The Buyuan River, the largest tributary of the Upper Mekong River, is one of the regions with the strongest climate change in the Mekong River Basin, and its cross‐border impact on water resources has received increasing attention. However, few studies have focused on the runoff responses to future climate change in the Buyuan River Basin (BRB). In this study, the MIKE SHE model was applied to estimate the runoff of the BRB, based on climate‐change scenarios from the Beijing Climate Center Climate System Model (BCC‐CSM1‐1) under two Representative Concentration Pathways (RCPs). The simulations predicted a slight decrease in the mean annual runoff of the BRB under the RCP4.5 and RCP8.5 scenarios (2020–2049) compared with the reference period (1959–2012), with an increase in the magnitude of inter‐annual variation, leading to increased deficit and surplus during dry and wet years. The simulations predicted the earlier maximum monthly runoff and a more evenly distributed annual distribution of the runoff under RCP4.5 and RCP8.5 scenarios as compared to the reference period. The arrival of the flood season in the BRB was advanced by a month under future climate‐change scenarios. The results showed that the average annual runoff of the BRB does not change significantly under future climate‐change scenarios. However, there are obvious changes in the inter‐annual and intra‐annual distribution of the runoff. [ABSTRACT FROM AUTHOR]