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Dynamical prediction of monsoon rainfall has been an important topic and a long-standing issue in both research and operational community. This paper provides a comprehensive evaluation of the subseasonal-to-seasonal (S2S) prediction skill of the East Asian summer monsoon (EASM) rainfall using the hindcast record from the Beijing Climate Center Climate System Model, BCC CSM1.1m, during the period 1983–2019. The model exhibits reasonable skills for predicting the EASM rainfall at all lead times with the skill dropping dramatically from the shortest lead time of about 2 weeks (LM0) to 1-month lead (LM1), and then fluctuating remarkably throughout 2-month to 12-month lead times. Over the EASM domain, the rapid decline of the S2S rainfall prediction skill from LM0 to LM1 is mainly caused by the inferior skills over Central China in July and over Northeast China in August. Composite analysis based on hindcast records suggest that these inferior skills are directly tied to the model's difficulties in capturing above-normal precipitation over eastern Central China and Northeast China in the respective months, which are further shown to be associated with anomalous weakening and meridional movement of the Northwestern Pacific subtropical high and the activity of large-scale teleconnection pattern hard to be predicted over northeastern Asia in summer, respectively. These findings inform the intrinsic limits of the S2S predictability of the EASM rainfall by a dynamical model, and strongly suggest that the level of confidence placed upon S2S forecasts should be stratified by large-scale circulation anomalies known to significantly affect the prediction skill, e.g., the subtropical high and high-latitude teleconnection patterns for summer monsoon rainfall prediction in this region. [ABSTRACT FROM AUTHOR]

A model‐circulation increment‐based dynamic statistical technique (MIDST) is proposed in this paper to improve the prediction of summer rainfall over southern China (SC) where quite low prediction skills have been found in the Beijing Climate Center Climate System Model version 1.1 with a moderate resolution (BCC_CSM1.1m). The results show that BCC_CSM1.1m can hardly represent the variability of the summer rainfall anomaly and its year‐to‐year increment over SC, and the skillful predictions are mostly confined over the middle reaches of the Yangtze River. Using the dynamic model output and statistical method, the MIDST is established to capture the coupled modes between the year‐to‐year increments of the summer rainfall anomaly and the associated simultaneous three‐dimensional coupled air‐sea circulation predictors. The cross‐validation indicates that the prediction skills of the MIDST are evidently improved for both the summer rainfall increment prediction and summer rainfall anomaly prediction compared with the direct BCC_CSM1.1m prediction. The skillful prediction can persist for long forecast leads over most regions except southwestern China. As the major predictability source of seasonal prediction, the intense response to changes in the circulation related to the El Niño‐Southern Oscillation (ENSO) is well captured, and thus, the performance improvement of the MIDST is primarily due to its more realistic representation of the incremental circulation related to the ENSO. [ABSTRACT FROM AUTHOR]

Q-SAT, a small spherical satellite designed and developed by Tsinghua university, was successfully launched on Aug 6, 2020 at Jiuquan satellite launch center in China. The mission of Q-Sat is the joint measurement of long-wavelength Earth gravity field and upper atmosphere density, which is of great significance for improving spacecraft orbit prediction and promoting the development of satellite gravity measurement technology. The satellite is designed in spherical shape innovatively and deploys high dynamic dual-frequency GPS as the main payload. On-orbit data shows that the atmospheric density detection precision is in the order of 10−14kg/m3 and the gravity recovery precision achieves the level of 30 orders. All mission success criteria and objectives were achieved. This paper presents the design, space mission, key technologies, science capabilities and preliminary results of the satellite. • Q-SAT, a spherical satellite for the joint measurement of long-wavelength Earth gravity field and upper atmosphere density, was launched in 2020. • An electromagnetic separation system with point connection-release structure is designed for the Q-Sat. • As secondary payload a powerful GPU module is implemented to validate artificial intelligence computing capabilities in orbit. • Modifications for the Jacchia-Roberts atmospheric density model are proposed in the paper. [ABSTRACT FROM AUTHOR]

Foggy weather is an important factor affecting highway traffic safety. Research on the automatic recognition method of highway fog visibility from surveillance images can provide technical support for the intelligent management and decision-making of the traffic management department. This paper analyzes multiple physical factors related to fog density based on the atmospheric scattering model and proposes a multi-channel fusion network that integrates these physical factors. Specifically, the method jointly exploits three streams to learn deep visual feature, transmission matrix feature and scene depth feature, and designs an attention fusion module to adaptively fuse these three streams for the final visibility level recognition, which is very beneficial for improving the recognition accuracy. Meanwhile, this paper constructs a synthetic dataset and a real-scene dataset for network parameters learning and performance evaluation. The images in the real-scene dataset are collected from surveillance videos of multiple highways in China. Experiments on these two datasets show that this method can identify visibility level more accurately than existing methods [ABSTRACT FROM AUTHOR]

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

Rice is a major staple food grain for more than half of the world's population, and China is the largest rice producer and consumer in the world. In a climate-warming context, the frequency, duration and intensity of heat waves tend to increase, and rice production will be exposed to higher heat damage risks. Understanding the negative impacts of climate change on the rice supply is a critical issue. In this study, a new perspective on agricultural weather index insurance is proposed to investigate the impact of extreme high-temperature events on rice production in South China in the context of climate change. Based on data from meteorological stations in Anhui Province in China from 1961 to 2018 and the projected data from five Global Climate Models under three representative concentration pathway (RCP) scenarios from 2021 to 2099, the spatial–temporal characteristics of heat stress and its influence on rice production were analyzed by employing a weather index insurance model. The interdecadal breakpoints in the trends of the heat stress weather insurance index (HSWI) and the payout from 1961 to 2018 in 1987 were both determined, which are consistent with the more significant global warming since the 1980s. The largest increase after 1987 was found in the southeastern part of the study area. The projected HSWI and the payout increased significantly from 2021 to 2099, and their growth was faster with higher radiative forcing levels. The HSWI values were on average 1.4 times, 3.3 times and 6.1 times higher and the payouts were on average 3.9 times, 9.8 times and 15.0 times higher than the reference values for the near future, mid-future and far future, respectively. The results suggest that a more severe influence of heat damage on rice production will probably happen in the future, and it is vital to develop relevant adaptation strategies for the effects of a warmer climate and heat stress on rice production. This paper provides an alternative way to transform the evaluation of the extreme climate event index into the quantitative estimation of disaster impacts on crop production. [ABSTRACT FROM AUTHOR]

To examine the characteristics of future precipitation under climate change is of great significance to urban water security. In this paper, multiple machine learning techniques, i.e., statistical downscaling model (SDSM), support vector machine (SVM), and multilayer perceptron (MLP), were used to downscale large-scale climatic variables simulated by the General Circulation Models (GCMs) to precipitation on a local scale. It was demonstrated in Shenzhen city, China, through multisite downscaling schemes based on projections from the Max Planck Institute Earth System Model (MPI-ESM1.2-HR), Meteorological Research Institute Earth System Model Version 2.0 (MRI-ESM2.0), and Beijing Climate Center Climate System Model (BCC-CSM2-MR). The obtained results showed that the downscaled precipitation would provide good monthly simulations against observations at 10 discrete stations. Regardless of superior performance of SVM and MLP over SDSM, the daily precipitation simulations should be further improved, and downscaling of heavy daily precipitations would be promoted by quantile mapping corrections. Due to the relatively poor simulation performance of BCC-CSM2-MR, the other two climate models were considered under the Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios) for ensemble precipitation projections for 2015–2100. Under the SSP1-2.6 scenario, the amounts of annual average precipitation for 10 stations were estimated to be higher relative to the historical period (2.7%–17%), and 9 out of 10 stations presented an increasing trend. However, downward trends also existed at three stations when it comes to scenarios SSP2-4.5 and SSP5-8.5. Moreover, a significantly positive trend was found to dominate the trend changes of annual extreme daily precipitation during 2015–2050, but the detected trends at stations were greatly dependent on the downscaling techniques and climate models. Besides, the increase in daily extreme precipitations for various return periods as well as statistically different precipitation characteristics for discrete stations would further shed light on urgent demands on urban resilient strategies for climate change adaptation. [ABSTRACT FROM AUTHOR]

The arid region of northwest China (ARNC) is one of the most sensitive areas to global warming. However, the performance of new Global Climate Models (GCMs) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating climate in this region, especially in the subregions, is not clear yet. Based on the temperature dataset from historical runs of CMIP6, this paper analyzed and evaluated the simulation ability of 29 GCMs in reproducing the annual mean temperature (tas), annual mean maximum temperature (tasmax) and annual mean minimum temperature (tasmin) in the ARNC and subregions from 1961 to 2014. The results show that (1) the correlation coefficients (CCs) between simulation and observation time series for the mean of two model ensembles (MME for equal-weight multi-model ensemble and PME for preferred-model ensemble) are generally better than those of 29 individual GCMs, with CCs ranging from 0.38 to 0.87 (p < 0.01). (2) All the models can simulate the significant warming trend of the three temperature elements in the study area well. However, the warming magnitude simulated by most of the models (41%) is smaller than the observations except for tasmax, which is also shown in the MME. (3) The spatial pattern of the three temperature elements can be better reflected by most models. Model simulation ability for the ARNC is better compared to that of the four subregions, with a spatial CC greater than 0.7 (p < 0.01). Among the subregions, the simulation performance of the north of Xinjiang for spatial pattern is superior to that of the other regions. (4) The preferred models for each subregion are various and should be treated differently when used. Overall, the PME outperforms both the MME and the individual models; it can not only simulate the linear trend accurately but also reduce the deviation effectively. [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]

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861–1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985–2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985–2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios. [ABSTRACT FROM AUTHOR]

Mesoscale convective systems (MCSs) are important warm-season precipitation systems in eastern China. However, our knowledge of their climatology and capability in their simulation is still insufficient. This paper examines their characteristics over the 2008–2017 warm seasons using convection-permitting climate simulations (CPCSs) with a 3-km grid spacing that explicitly resolves MCSs, as well as a high-resolution gauge-satellite merged precipitation product. An object-based tracking algorithm is applied to identify MCSs. Results indicate that the MCS genesis and occurrence are closely related to the progression of the East Asian monsoon and are modulated by the underlying topography. On average, about 243 MCSs are observed each season and contribute 19% and 47% to total and extreme warm-season precipitation. The climatological attributes and variabilities are reasonably reproduced in the CPCS. The major model deficiencies are excessive small MCS occurrence and overmuch MCS rainfall, consequently overestimating the precipitation contributions, whereas observational uncertainties may play a role too. Both the observed and simulated MCS precipitation feature a nocturnal or morning maximum and an eastward delayed diurnal peak east of the Tibetan Plateau, in contrast to the dominant afternoon peak of non-MCS precipitation. The favorable comparison with observations demonstrates the capability of CPCSs in simulating MCSs in the Asian monsoon climate, and its usefulness in projecting the future changes of MCSs under global warming. The finding that non-MCS precipitation is responsible for the high biased afternoon precipitation provides helpful guidance for further model improvement. [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]

• Atmospheric segmentation model is proposed considering aerosol optical depth. • Proposed model improves the accuracy of radiative cooling potential prediction. • Relative error of the predicted and experimental radiative cooling potential is 2.4%. • Map of radiative cooling potential in China is derived based on the proposed model. Radiative cooling offers a wide range of potential applications in the current promotion of clean energy. However, the radiative cooling potential is generally underestimated, accounting for ignoring the effect of aerosols on atmospheric longwave downward radiation. This paper proposes an atmospheric segmentation model to improve the accuracy of radiative cooling potential prediction. The heat transfer on the radiative cooling surface is analyzed, and the accuracy of the proposed atmospheric segmentation model is verified with experiments and compared with the models in existing studies. Then, the effects of aerosol optical depth, precipitable water vapor, and relative humidity on the radiative cooling potential are discussed in detail based on the atmospheric segmentation model. Finally, the map of radiative cooling potential in China is also derived from the proposed atmospheric segmentation model. The results show that the relative error of radiative cooling power between the atmospheric segmentation model prediction and experiment is only 2.4%. Furthermore, the measured and calculated atmospheric longwave downward radiations match well with each other, in which the Pearson correlation coefficients and root mean square errors are 0.9834 and 11.34 W/m2 under clear sky conditions and 0.94211 and 21.32 W/m2 under cloudy sky conditions. [ABSTRACT FROM AUTHOR]

The increasing penetration of renewable energy leads to seasonal fluctuation in the power system. This also results in continuous low-renewable-output events, which pose significant challenges for ensuring adequate renewable energy supply and arouse growing concern. To analyze the seasonal fluctuation characteristics of renewable in different timescales, this paper first decomposes the renewable time series into three components: climate, seasonal, and daily. Then an Itô process is introduced to model the climate and daily components considering the time series' stochastic characteristics and time correlations. Finally, the proposed model is first tested based on case studies on historical data from two selected weather stations in China. Moreover, the method is further applied to analyzing data from 253 weather stations across China at the provincial level. The results indicate that low-renewable-output events are more likely to have a longer duration in central China. [ABSTRACT FROM AUTHOR]

Probabilistic load forecasting (PLF) are of critical importance to reliable and economical power system operations. To further improve the PLF performance, this paper extends the load forecasting model with high accuracy to PLF by using Monte Carlo (MC) Dropout and soft thresholding (ST) for uncertainty modeling and noise reduction, respectively. Specifically, the method first builds a Dual-stage Attention-based Long and Short Time Pattern Network (DA-LSTPNet) by weighting the temporal information extracted from Gate Recurrent Unit (GRU) and convolutional neural networks (CNN) through the time attention mechanism(TAM). Then, an adaptive ST structure is used to reduce the impact of meteorological data noise on model prediction performance. The term ST-DA-LSTPNet refers to DA-LSTPNet with the addition of the ST structure. Finally, an appropriate Dropout setting is inserted in the neural network layers during the model's testing phase to achieve the model parameters' stochastic process. After multiple sampling and calculations, the final PLF result is obtained. To demonstrate the effectiveness of the proposed method, this work construct a case study using the actual data from a prefecture-level city in southern China. Compared with several benchmark methods, the proposed method significantly improves the performance of PLF and has good anti-noise generalization ability. [Display omitted] • The high-accuracy load forecasting model extends to probabilistic load forecasting. • The adaptive soft thresholding can mitigate noise impact on prediction. • The model performs well on actual grid data, with over 92% prediction coverage. • This work provides effective support for economic scheduling and management. [ABSTRACT FROM AUTHOR]

This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. In response to soil moisture increase, the local air temperature significantly decreases, and precipitation increases in summer. The wetter ANWC results in a warmer and rainless climate over the Central part of North China Plain (CNCP). While the influence of increasing soil moisture on the Tibetan Plateau (TP) precipitation is negligible due to the high altitude. A cooler climate over ANWC leads to an abnormal descending motion occurring. Meanwhile, an anomalous high pressure and an abnormal anticyclonic circulation occurs in 500 hPa over the west of ANWC. Correspondingly, a huge anomalous cyclone circulation occurs over the northeastern part of China and Mongolia, which can hinder the northward movement of the East Asian summer Monsoon (EASM) and reduce the water vapor transport into Central China Plain. As a result, the weakened EASM thus leads to rainless over CNCP. As soil moisture increases over ANWC is stronger, the magnitudes of the influence on the atmospheric field become larger. This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. • This paper examines the local and non-local climatic effects of wetter arid areas of Northwestern China (ANWC) in summer using a fully coupled climate model. • It finds that the wetter ANWC can result in a warmer and rainless climate over the Central part of North China Plain (CNCP) through a complex atmospheric circulation response. • A cooler climate leads to an abnormal descending motion occurring over ANWC. • A huge anomalous cyclone circulation over the northeastern part of China and Mongolia can block the East Asian summer Monsoon northward and decrease the water vapor transport into Central China Plain. • This study highlights the wetter ANWC effect on local climate and provides more information about the mechanism of wetter ANWC remote effect on non-local climate. [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]

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]

The western Tianshan Mountains region in China has a complex topography where basins, mountains and glaciers co-exist. It is of great significance to study the sensitivity of meteorological factors in this region to different parameterization schemes of climate models. In this paper, the regional climate model RegCM4.5 is used to simulate the meteorological factor (mean temperature, maximum temperature, minimum temperature, precipitation and wind speed) occurring in the western Tianshan Mountains region from 2012 to 2016, so as to investigate the effects of different cumulus convective schemes (Grell, Tiedtke and Emanuel), including land cumulus convective schemes (LCCs) and ocean convective schemes (OCCs) on annual and seasonal simulations of meteorological factor by using the schemes of RUN1 (Grell for LCC and Tiedtke for OCC), RUN2 (Tiedtke for LCC and Emanuel for OCC), RUN3 (Grell for LCC and Emanuel for OCC) and ENS (the ensemble of RUN1, RUN2 and RUN3). The results show that the simulations of annual and seasonal meteorological factors are not significantly sensitive to the combination of LCCs and OCCs. In the annual simulations, RUN2 scheme has the best simulation performance for the maximum, average and minimum temperatures. However, other schemes of precipitation simulation outperform RUN2 scheme, and there is no difference among the four schemes for wind speed simulation. In the seasonal simulations, RUN2 scheme still performs well in the simulation of the average, maximum and minimum temperatures for four seasons, except for the simulation of the average temperature in spring and summer. For the simulation of the maximum temperature in summer, RUN2 scheme performs the same as ENS. For the simulation of other seasons, different meteorological factors have different performances in four seasons. Overall, the results show that different combinations of cumulus convection schemes can improve the simulation performance of meteorological factors in the western Tianshan Mountains of Xinjiang. [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]