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Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4 % of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation. New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

ABSTRACTPassive microwave (PM) remote sensing have been extensively used for snow depth (SD) estimation. However, current SD products from traditional PM data fail to capture the differentiation in mountainous and complex terrains with coarse resolution. Therefore, this study incorporates factors such as geographical location, topographic features, and land cover, along with various machine learning algorithms including Gaussian process regression (GPR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to construct and optimize SD estimation using enhanced-resolution PM data. The results demonstrate the following: (1)With the auxiliary variables, the SD product from LightGBM-based models exhibits the highest accuracy. (2)The performance of SD products from the LightGBM-based model varies monthly and annually, with shallow snow cover being slightly overestimated (30 cm). (3)The reliable SD product indicates spatial distribution characteristics in Xinjiang, with regions demonstrating no significant improvement being larger than those with no significant degradation. The above results illustrate the remarkable advantages of machine learning in capturing SD distribution and its spatio-temporal variation bolstered by enhanced PM data and auxiliary data.

The Tarim Basin in the western part of Northwest China (NWC) is the largest inland basin in the world and one of the most arid regions in the middle latitudes. In recent years, heavy precipitation events have occurred frequently in this region, especially in the western Tarim Basin (WTB), due to the climate change. Based on the hourly precipitation data from 2010 to 2022, the diurnal variation in summer precipitation and the characteristics of precipitation events with different durations in WTB have been analyzed. The results mainly show that (1) the diurnal variations in the precipitation amount (PA), precipitation frequency (PF) and precipitation intensity (PI) mainly present a unimodal pattern, but the times of maximum value do not coincide. The peak value of PA and PF appears between 01:00 and 03:00 BJT (Beijing Time), while the valley value appears around 18:00 BJT, yet the peak value of PI appears between 20:00 and 23:00 BJT with no obvious valley value. (2) There are some differences in the diurnal variation characteristics of precipitation among different summer months and different regions. (3) During the past decade, the precipitation structure in WTB has been continuously adjusted, and short-duration- and long-duration-precipitation-dominant periods appear alternately. On the whole, short-duration precipitation has been more frequent in summer, accounting for 70% of the total precipitation events and 40% of the total accumulated precipitation amount. These results can help us to better understand the refined physical characteristics of precipitation events and enhance our understanding of the local climate in the WTB under the background of climate change.

The impact of changing climate on vegetation in dryland is a prominent focus of global research. As a typical arid region in the world, Central Asia is an ideal area for studying the associations between climate and arid-area vegetation. Utilizing data from the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ECMWF ERA-5) and normalized difference vegetation index (NDVI) datasets, this study investigates the spatio-temporal variation characteristics of the NDVI in Central Asia. It quantitatively assesses the contribution rates of climatic factors to vegetation changes and elucidates the impact of an increased vapor pressure deficit (VPD) on vegetation changes in Central Asia. The results indicate that the growing seasons’ NDVI exhibited a substantial increase in Central Asia during 1982–2015. Specifically, there was a pronounced “greening” process (0.012/10 yr, p < 0.05) from 1982 to 1998. However, an insignificant “browning” trend was observed after 1998. Spatially, the vegetation NDVI in the growing seasons exhibited a pattern of “greening in the east and browning in the west” of Central Asia. During spring, the dominant theme was the “greening” of vegetation NDVI, although there was noticeable “browning” observed in southwest region of Central Asia. During summer, the “browning” of vegetation NDVI further expanded eastward and impacted the entire western Central Asia in autumn. According to the estimated results computed via the partial differential equation method, the “browning” trend of vegetation NDVI during the growing seasons was guided by increased VPD and decreased rainfall in western Central Asia. Specifically, the increased VPD contributed 52.3% to the observed vegetation NDVI. Atmospheric drought depicted by the increase in VPD significantly lowers the “greening” trend of vegetation NDVI in arid regions, which further aggravates the “browning” trend of vegetation NDVI.

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism.

Abstract The significant socioeconomic impact of extreme flooding provides an incentive to improve our understanding of flood drivers. In this study, floods that occurred in northern Xinjiang from 2006 to 2011 were divided into three categories: rainstorm‐type, warming‐type, and mixed‐type. These three types of floods primarily occurred from April to July, with most occurring in May and June. Through analysis of the atmospheric circulation evolution process of the three types of floods, it can be concluded that when a rainstorm‐type flood occurs, northern Xinjiang is affected by an anomalous cyclone that forms in front of the strengthened trough over northern Europe. Anomalous cyclones provide favorable conditions for precipitation, which is conducive to rainstorm‐type floods. As for the warming‐type flood event, northern Xinjiang is affected by an anomalous anticyclone formed by the eastward movement of the blocking system in the middle of the Eurasian continent. Before the third type of mixed flood event occurred, northern Xinjiang was affected by an anomalous cyclone formed by energy propagation along the northwesterly wind belt. In addition, the energy propagating along the westerly wind belt along the southern road is conducive to the formation of a high‐pressure ridge in southern Xinjiang. In addition, the analysis of temperature conditions indicates that the daily maximum temperature showed a warming trend from 5 to 1 day before the warming‐type and mixed‐type flood event occurred. These results provide valuable insights for flood risk management by identifying atmospheric circulation patterns and temperature conditions associated with floods in northern Xinjiang.

The Taklimakan Desert (TD) is the largest desert in China located in the Tarim Basin (TB) in China’s arid region. This study is a review of the change in precipitation and its extremes since 1961 and the high-impact extreme precipitation events in 2012–2021, particularly in 2021, with a focus on the TD along with the surrounding oases and mountainous regions.The TB has experienced significantly warmer and wetter trends since 1961, and extreme rainfall has increased significantly in the TD and its surrounding areas during the 2000s. In the TB, the year 2021 was identified as the 4th warmest for 1961–2021, and was remembered for unprecedented extreme events. Three high-impact extreme events that occurred in 2021 are highlighted, including extreme heavy rainfall over Hetian in mid-June. The earliest extreme rainfall event occurred over North Bazhou in early spring, and the strongest heavy snowfall over Baicheng in April. In addition, we also discussed the underlying physical mechanisms of extreme events over the TB and proposed novel perspectives and unresolved questions on the sciences of heavy rainfall in arid regions. Our results provide a reference for the physical mechanism, attribution, and high-resolution modeling of extreme events.

The convection initiation (CI) mechanisms of severe storms have received increasing attention because severe storms have been occurring more frequently around the globe in recent years. In this work, the CI mechanisms of severe convective weather associated with a gust front (GF) which occurred on 9 July 2016, near the Korla at the northern edge of the Tarim Basin, Xinjiang, is investigated using observational data including Doppler weather radar data and automatic weather stations data, and high-resolution numerical simulation data. The results showed that, during the eastward movement of the GF, a number of convective cells were successively triggered in the vicinity of the GF, which developed rapidly and continuously merged with the convective system from behind, resulting in the further development and maintenance of this convective system. According to the diagnostic analysis of vertical acceleration which can be decomposed into dynamic acceleration (ad) and buoyant acceleration (ab), it was found that both ad (up to ~4 × 10−3 m s−2) and ab (up to ~7 × 10−3 m s−2) made positive contributions to the CI. Further analyses based on the decompositions of the ad and ab revealed that the extension term was the main contributor for the ad, while the warming of the dry air due to the release of latent heat from the precipitation condensate made the major contribution to the ab. The extension term indicates the elevated convergence jointly induced by the airflow of mid-level horizontal convective rolls (MHCRs) and updraft flow near the leading edge of the GF. The jointly induced elevated convergent updraft can be considered to be an important contributor for the CI mechanisms.

Extreme precipitation events have been occurring frequently worldwide, and their causative factors and convection initiation (CI) mechanisms have been attracting more and more attention in recent years. As a comprehensive study on the CI mechanisms of extreme rainstorms over the northern slope of the Kunlun Mountains (KLM), Xinjiang, based on both observational and high tempo-spatial numerical simulation, the major findings of this work are as follows: A cold pool (CP) was formed in the northwestern Tarim Basin under the influence of early precipitation evaporation, and it moved towards the northern slope of the KLM several hours before the CI. With the movement of the CP, a significant vertical temperature gradient was formed close to the leading edge of the CP, thereby enhancing local convective instability (up to ~10 PVU). In addition, the vertical shear of the horizontal winds at the leading edge of the CP led to a notable increase in the baroclinic component of moist potential vorticity, thus reinforcing the local conditional symmetric instability (up to ~8 PVU), providing another important unstable energy for the CI. In addition, the combined effect of the convergent lifting of a boundary layer jet (BLJ, the maximum wind speed below 1 km exceeding 10 m s−1) and the significant frontogenetical forcing (up to ~100 × 10−8 K m−1 s−1) at the leading edge of the CP were the causes of the release of the unstable energies. Further analysis of the frontogenetical forcing associated with the CP indicates that the convergence (up to ~2 × 10−3 s−1), diabatic heating and slantwise terms (indicates the baroclinicity and inhomogeneity of the vertical momentum in horizontal direction) were the major contributors, whereas the deformation term at the leading edge of the CP provided a relatively weaker contribution.

This study performed an observational analysis to examine the interdecadal variation in the frequency of extreme high-temperature events (EHEs) during spring over the western Tianshan mountain, China, which were characterized by relatively fewer (more) EHEs during 1983–1996 (2000–2015). A composite analysis indicated that the interdecadal increase in EHEs is closely related to a deep dynamic anomalous Iranian high. Under the control of this high system, the water vapor content decreased over the western Tianshan mountains, and atmospheric circulation was dominated by a descending motion. Both were attributed to the decreased cloud cover, inducing a cloud-forced net solar radiation increase. The short-wave radiation flux and sensible heat flux reaching the surface increased, and the net surface heat flux increased cumulatively, which was conducive to the surface temperature increase and EHE occurrence. The anomalous Iranian high responsible for ECEs occurrence was related to the air-sea interaction over the Atlantic and Indo-Pacific. The latitudinal sea surface temperature (SST) difference between the tropical western Pacific and the western Indian Ocean directly strengthens the Walker circulation and thus enhances the Iranian high. In addition, the anomalous Iranian high was affected by the atmospheric wave trains at middle latitude, which was triggered by the warm anomaly of the Atlantic SST.

As one of the most vulnerable types of global ecosystems and water resource systems, arid regions are most sensitive to climate change. The Xinjiang (XJ) region is an important part of the arid region in Central Asia and is representative of global arid regions. The complex topography and underlying surface result in distinct climate change characteristics in XJ. In this study, XJ was divided into five sub-regions: the Irtysh River Basin (IRB), the economic belt on the northern slope of the Tianshan Mountains (NSTM), the Ili River Basin (ILRB), the Turpan-Hami Basin (THB), and the Tarim River Basin (TRB). The change in temperature and precipitation over XJ and its sub-regions were investigated from 1960 to 2019 using the Mann-Kendall method and cross-wavelet analysis. Moreover, the multi-timescale correlations between the variations in temperature and precipitation and the atmospheric circulation indices were explored. The results show significant warming and wetting trends in XJ from 1960 to 2019. The warming rate was 0.32°C/10 a (p < 0.01), with an abrupt change during the mid-1990s. The increasing rate of precipitation was 9.24 mm/10 a (p < 0.01), with an abrupt change during the middle to late 1990s. In terms of seasonal variation, the greatest warming rate was during winter (0.37°C/10 a), whereas the precipitation increase was concentrated in summer (3.48 mm/10 a). In terms of spatial variation, a significant warming trend was observed in THB, IRB, ILRB, and NSTM, and precipitation increased significantly in ILRB, NSTM, and the western TRB in southern XJ. The Hurst index analysis indicated that the warming and wetting trends in XJ will slow in the future. Climate change in XJ was closely related to atmospheric circulation at multiple timescales. The subtropical high, Northern-Hemisphere polar vortex activities and the Tibetan Plateau have a significant impact on climate change in XJ. The annual mean temperature in XJ was positively correlated with the area and intensity index of the subtropical high over North Africa, Atlantic, and North America, and negatively correlated with the area and intensity index of the Asia polar vortex. The XJ annual precipitation was positively correlated with the index of the Tibet Plateau Region one and negatively correlated with the intensity index of the Atlantic and European polar vortex, and the area and intensity index of the Northern Hemisphere polar vortex. The results of this study can provide some references for the scientific assessment and accurate prediction of climate change in XJ.

The extreme precipitation threshold is fundamental to extreme precipitation research, directly affecting the cognition of extreme characteristics. Based on the daily precipitation data of 62 meteorological stations from 1985 to 2005, this study uses parametric and non-parametric approaches to determine the extreme precipitation threshold in Central Asia, analyzes the statistics and spatial distribution of different threshold criteria, and discusses the trend of extreme precipitation. The capability of the grid dataset of APHRODITE and GPCC in the extreme precipitation analysis in Central Asia is evaluated from the threshold perspective. The results are as follows: 1) Contrary to the parametric approach, the threshold determined by the percentile indices in the warm season is slightly higher than in the cold season. The mean threshold of the warm (cold) season in Central Asia is defined by the 95th percentile index and the 10-year return period, which are 14.0 mm (13.5 mm) and 24.2 mm (25.7 mm), respectively. 2) The spatial distribution of extreme precipitation threshold in Central Asia is higher in the southeast and lower in the north during the cold season; In the warm season, it is high in the north and southwest and low in the center. 3) Although both APHRODITE and GPCC datasets can basically reproduce the spatial distribution of extreme precipitation threshold, they underestimate the magnitude of the threshold, especially APHRODITE. 4) There is no obvious extreme precipitation trend in Central Asia during the study period. Furthermore, the trend in characteristics of extreme precipitation based on different thresholds shows a consistent trend in time but not spatially. We suggest that the threshold selection should adjust the balance between sufficient samples and extreme values according to actual conditions. The results of this study can provide a reference for extreme precipitation threshold criteria under specific application conditions in Central Asia.

Changes in lake water resources and regional hydrological processes in response to climate change and human activity necessitates timely and accurate access to lake change information to monitor water cycles and water security. The Ebinur Lake Basin has experienced a serious ecological crisis in recent years, which is majorly due to excess use of its water. Therefore, in this study, we used Ebinur Lake as a research object, and used Geographic Information System and remote sensing technology, Landsat, Sentinel, and MODIS images, the Google Earth Engine platform, and the water body index method to determine the changes in lake area from April to October of 2011–2020. Daily data from the Alashankou and Jinghe meteorological stations from 2011 to 2020 were collected. The center of gravity-geographically and temporally weighted regression model was used to analyze the factors changes in surface area. The results showed obvious spatial and temporal heterogeneities for the surface area. Except for 2016, which had unusual/extreme weather, in the last decade, the lake surface area generally showed a monthly decreasing trend from April to October. When Ebinur Lake is one water body, the surface area ranges between 530 and 560 km2. We concluded that the spatio-temporal characteristics of Ebinur Lake can be divided into two levels. When the southeast and northwest regions of the lake merge, it represents the largest possible surface area of Ebinur Lake; this was called the “water storage level”. Historically, Ebinur Lake has lost significant amounts of water, and there is no water body connecting the two main parts of the lake (thereby dividing the lake into two areas); this was called “water demand level”. The trajectory of the center of gravity of the lake is linear, with a slope of 45° and a direction of northwest to southeast. The lake gravity center has different aggregation states. According to the season, it can be divided into spring gathering and autumn gathering. The variation in the surface area of Ebinur Lake is highly correlated with the meteorological and hydrological variation during the year. The highest correlation was observed between lake surface area and wind speed. This study aimed to supplement other studies that explore the lake annually and provide a reference for future water resources management and planning.

The response of spatial and temporal vegetation changes to runoff is a complex process involving the interaction of several factors and mechanisms. Timely and accurate vegetation and runoff change information is an important reference for the water cycle and water resource security. The Ebinur Lake Basin is representative of arid areas worldwide. This basin has been affected by climate change and human activities for a long time, resulting in the destruction of the basin’s ecological environment, and especially its vegetation. However, there have been few studies that have focused on watershed vegetation and runoff changes. Therefore, we combined Generalized Information System and remote sensing technology, used SWAT and InVEST models based on the Google Earth Engine platform, and used the vegetation normalization index method to calculate the spatial distribution of vegetation and water production from 2000 to 2020 in Ebinur Lake. Sen’s trend analysis and the M–K test were used to calculate vegetation and runoff trends. The relationship between vegetation and runoff variation was studied using bivariate spatial autocorrelation based on sub-basins and plant types. The results showed that the Z parameter in the InVEST model spanned from 1–2. The spatial distribution of the water yield in a watershed is similar to the elevation of the watershed, showing a trend of higher altitude leading to a higher water yield. Its water yield capacity tends to saturate at elevations greater than 3500 m. The local spatial distribution of the Normalized Difference Vegetation Index(NDVI) values and water yield clustering in the watershed were consistent and reproducible. Interannual runoff based on sub-basins correlated positively with the overall NDVI, whereas interannual runoff based on plant type correlated negatively with the overall NDVI.

Understanding the precipitation variability and extreme precipitation over arid Central Asia (CA) has largely been hampered by the lack of daily precipitation observations. The gridded precipitation datasets over CA are large discrepancies. Here, three gauge-based gridded daily precipitation products from Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climate Prediction Center Based Analysis of Global Daily Precipitation (CPC_global) were assessed and compared with 49 rain gauge daily observations precipitation (OBS) from January 1985 to December 2015 using different time-scales over CA and different climate regimes, specifically Northern CA with temperate continental climate (NCA), Southwestern CA with dry arid desert climate (SWCA), and Southeastern CA with Mediterranean continental climate (SECA). Four accuracy indices [correlation coefficient (R), Bias, root mean square error (RMSE), and relative bias (RBias)] were employed to evaluate the performance of the three products in depicting the spatiotemporal features of precipitation variation over CA at multiple time scales (including daily, monthly, seasonal, and yearly). The mean annual and daily precipitation of OBS and three gridded products exhibit the trend of a gradual precipitation decreased from SECA to NCA and SWCA. The best overall performance was obtained for APHRODITE and GPCC for daily and annual time-scale, whereas CPC shows noticeable underestimation precipitation in SECA. The monthly precipitation depicted distinct features with a bimodal pattern with a peak in March and another in December, include the SECA and SWCA regions. In contrast, precipitation was concentrated in summer with the peak in July over the NCA region. At monthly scale terms, APHRODITE was more accurate in the wet seasons (winter and spring months) in SWCA and SECA. Additionally, GPCC has fairly better capability in summer months in NCA. Considering the spatial distribution, the bias variability was largerly in mountainous areas than in the plains. Temporally, the bias largerly in the dry seasons than in the wet seasons. At the interannual variability scale, GPCC was capable of qualitatively increasing the CA (NCA and SECA) precipitation during the last 21 years, while APHRODITE underestimated the trends. The CPC overestimated the precipitation trends over all regions. This study can serve as a reference for selecting daily precipitation products with low densities of stations, complex topographies, and similar climatic regions.

The Tibetan Plateau is among the region’s most sensitive areas to global climate change. The observation data from 113 meteorological stations on the Tibetan Plateau and surrounding regions in China for 1971–2017 were used to analyze the periodic oscillations and trends in precipitation and extreme precipitation on multiple time scales to ensemble empirical mode decomposition. The relationship between extreme precipitation and sea-surface temperature (SST) anomalies was also explored. The results were as follows. (1) The timing of extreme-precipitation events in the highlands is consistent, with increased total precipitation and increased frequency, intensity, and extreme values of extreme precipitation. (2) Changes in temperature and precipitation are not completely synchronized. The total extreme precipitation, number of extreme-precipitation days, maximum single-day precipitation, and extreme single-day precipitation intensity all showed increases with fluctuations; the quasi-3-year oscillation contributes the most to the extreme precipitation. PRCPTOT is most strongly correlated with R10 and R95p. (3) The spatiotemporal patterns of the first and second empirical orthogonal function modes of the indices differed significantly and were not spatiotemporally uniform, but exhibited local clustering. (4) The Indian Ocean Warm Pool Strength and Western Pacific Warm Pool Strength indices were most highly correlated with each extreme-precipitation index, and the timings of the extreme-precipitation events lagged behind those of the SST anomalies. This study improves our understanding of extreme precipitation events in the context of climate warming and provides a basic analysis for the further assessment and prediction of extreme precipitation on the Tibetan Plateau and the surrounding ecologically fragile areas.

Central Asia is one of the driest regions in the world with a unique water cycle and a complex moisture transport process. However, there is little information on the precipitation δ18O content in Central Asia. We compiled a precipitation δ18O database from 47 meteorological stations across Central Asia to reveal its spatial-temporal characteristics. We determined the relationship between precipitation δ18O and environmental variables and investigated the relationship between δ18O and large-scale atmospheric circulation. The Central Asia meteoric water line was established as δ2H = 7.30 δ18O + 3.12 (R2 = 0.95, n = 727, p < 0.01), and precipitation δ18O ranged from +2‰ to −25.4‰ with a mean of −8.7‰. The precipitation δ18O over Central Asia was related to environmental variables. The δ18O had a significant positive correlation with temperature, and the δ18O-temperature gradient ranged from 0.28‰/°C to 0.68‰/°C. However, the dependence of δ18O on precipitation was unclear; a significant precipitation effect was only observed at the Zhangye and Teheran stations, showing δ18O-precipitation gradients of 0.20‰/mm and −0.08‰/mm, respectively. Latitude and altitude were always significantly correlated with annual δ18O, when considering geographical controls on δ18O, with δ18O/LAT and δ18O/ALT gradients of −0.42‰/° and −0.001‰/m, respectively. But both latitude and longitude were significantly correlated with δ18O in winter. The relationship between δ18O and large-scale atmospheric circulation suggested that the moisture in Central Asia is mainly transported by westerly circulation and is indirectly affected by the Indian monsoon. Meanwhile, the East Asian monsoon may affect the precipitation δ18O content in westerly and monsoon transition regions. These results improve our understanding of the precipitation δ18O and moisture transport in Central Asia, as well as the paleoclimatology and hydrology processes in Central Asia.

Satellite-derived vegetation records (GIMMS3g-NDVI) report that climate warming promotes vegetation greening trends; however, the climate impacts on vegetation growth during the global warming hiatus period (1998–2012) remain unclear. In this study, we focused on the vegetation change trend in Xinjiang in spring and autumn before and during the recent warming hiatus period, and their climate-driving mechanisms, which have not been examined in previous studies. Based on satellite records, our results indicated that the summer normalized difference vegetation index (NDVI) in Xinjiang experienced a greening trend, while a browning trend existed in spring and autumn during this period. The autumn NDVI browning trend in Xinjiang was larger than that in spring; however, the spring NDVI displayed a higher correlation with climatic factors than did the autumn NDVI. During the warming hiatus, spring climatic factors were the main controlling factors of spring NDVI, and spring vapor pressure deficit (VPD) had the highest positive correlation with spring NDVI, followed by spring temperature. The larger increase in air temperature in spring than in autumn resulted in increased VPD differences in spring and autumn. In autumn, summer climatic factors (e.g., VPD, WS, RH, and precipitation) were significantly correlated with the autumn NDVI during the warming hiatus. However, the autumn temperature was weakly correlated with the autumn NDVI. Our results have significant implications for understanding the response of vegetation growth to recent and future climatic conditions.

The dynamics of the ecosystem represented by vegetation under the influence of human activities have become an important issue in the study of the regional ecological environment. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. Xinjiang is one of the most ecologically fragile areas in the world, and vegetation changes have received extensive attention. However, the spatiotemporal patterns and evolutionary trends of anthropogenic impacts on vegetation dynamics in Xinjiang are still unclear. In this study, the anthropogenic impacts on vegetation dynamics were quantitatively assessed by combining the improved normalized difference vegetation index (NDVI) prediction model and the residual analysis method in Xinjiang, China. The human driving factors were analyzed with the support of a stepwise multiple regression model for vegetation changes at the county scale. Based on trend analysis and the Hurst exponent, the spatiotemporal characteristics and evolutionary trends of the impact of human activities on vegetation change were discussed. The results show that (1) the NDVI values in Xinjiang showed a gradually increasing trend at a rate of 0.005/10 years from 1982 to 2018, and the vegetation dynamics mainly showed significant improvements (57.09% of the vegetated areas), especially for crops. (2) The anthropogenic effects of vegetation changes in Xinjiang mainly included positive impact increases (43.22% of the vegetated areas) from 2000 to 2018. Human activities promoted the increase in the NDVI of various vegetation types. Both the positive and negative impacts of human activities increased over the study period, and the growth rate of the positive influence (0.08%/10 years) was higher than that of the negative influence (0.04%/10 years). (3) The cultivated area, GDP of primary industry, and population are the main anthropogenic factors causing the increase in NDVI, which dominate the vegetation greening in 30.34%, 29.22%, and 28.09% of the counties in Xinjiang, respectively. The animal husbandry population, agricultural population, and livestock number are the main anthropogenic factors causing the decrease in NDVI, which dominate the vegetation degradation in 23.60%, 21.35%, and 17.98% of the counties in Xinjiang, respectively. (4) The evolutionary trend of the anthropogenic impact on vegetation dynamics in Xinjiang will be dominated by anti-persistence (53.84% of the vegetated areas), thereby mainly showing that the positive impacts continued to increase (22.56% of the vegetated areas), especially for crops, shrubs, grasslands, and alpine vegetation. Our results are helpful in understanding the characteristics and evolutionary trends of vegetation changes in arid areas caused by human activities and are of significance as a reference for policymakers to appropriately adjust policy guidance in a timely manner to promote the protection and sustainable development of fragile ecosystems.

The Tibetan Plateau (TP) are regions that are most sensitive to climate change, especially extreme precipitation changes with elevation, may increase the risk of natural disasters and have attracted attention for the study of extreme events in order to identify adaptive actions. Based on daily observed data from 113 meteorological stations in the Tibetan Plateau and the surrounding regions in China during 1971–2017, we calculated the annual total precipitation and extreme precipitation indices using the R ClimDex software package and explored elevation-dependent precipitation trends. The results demonstrate that the annual total precipitation increased at a rate of 6.7 mm/decade, and the contribution of extreme precipitation to total precipitation increased over time, and the climate extremes were enhanced. The annual total, seasonal precipitation, and precipitation extreme trends were observed in terms of elevation dependence in the Tibetan Plateau (TP) and the surrounding area of the Tibetan Plateau (TPS) during 1971–2017. There is growing evidence that the elevation-dependent wetting (EDWE) is complex over the TP. The trends in total precipitation have a strong dependence on elevation, and the EDWE is highlighted by the extreme precipitation indices, for example, the number of heavy precipitation days (R10) and consecutive wet days (CWD). The dependence of extreme precipitation on elevation is heterogeneous, as other extreme indices do not indicate EDWE. These findings highlight the precipitation complexity in the TP. The findings of this study will be helpful for improving our understanding of variabilities in precipitation and extreme precipitation in response to climate change and will provide support for water resource management and disaster prevention in plateaus and mountain ranges.

Observations indicate that temperature and precipitation patterns changed dramatically in Xinjiang, northwestern China, between 1961 and 2015. Dramatic changes in climatic conditions can bring about adverse effects. Specifically, meteorological drought severity based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) showed a decreasing trend in Xinjiang prior to 1997, after which the trend reversed. SPEI-based drought severity shows a much stronger change during 1997–2015 than the SPI, which is independent of the effect of evaporative demand. Meteorological drought severity has been aggravated by a significant rise in temperature (1.1 °C) over the last two decades that has not been accompanied by a corresponding increase in precipitation. As a result, the evaporative demand in Xinjiang has risen. An examination of a large spatio-temporal extent has made the aggravated drought conditions more evident. Our results indicate that increased meteorological drought severity has had a direct effect on the normalized difference vegetation index (NDVI) and river discharge. The NDVI exhibited a significant decrease during the period 1998–2013 compared to 1982–1997, a decrease that was found to be caused by increased soil moisture loss. A positive relationship was recorded between evaporative demand and the runoff coefficients of the 68 inland river catchments in northwestern China. In the future, meteorological drought severity will likely increase in arid and semiarid regions as global warming continues.

This research analyzed the spatiotemporal patterns of drought in Xinjiang (northwestern China) between 1961 and 2015 using the standardized precipitation evapotranspiration index (SPEI). Furthermore, the correlations between Atlantic Multidecadal Oscillation (AMO)/El Niño⁻Southern Oscillation (ENSO) events and drought were explored. The results suggested an obvious trend toward aggravated drought, with a significant inflection point in 1997, after which the frequency of drought increased sharply. Spatially, the increase in drought occurred largely in southern and eastern Xinjiang, where occurrences of moderate and extreme drought have become more frequent during the last two decades, whereas northwestern Xinjiang and the Pamir Plateau showed wetting trends. Empirical orthogonal function analysis (EOF) of drought patterns showed a north⁻south antiphase and an east⁻west antiphase distribution. The positive (negative) phase of the AMO was related to increased (decreased) drought in Xinjiang, particularly after 1997. During a warm phase (El Niño), major droughts occurred over northern Xinjiang, but they lagged by 12 months. However, not all El Niño and La Niña events were responsible for drought events in northern Xinjiang during this period, and other drivers remain to be identified. This study suggests the possibility of AMO and ENSO links to drought in Xinjiang, but further analysis is needed to better understand such mechanisms.