The Chinese Tianshan mountainous region (CTMR) is a typical alpine region with high topographic heterogeneity, characterized by a large altitude span, complex topography, and diverse landscapes. A significant increase in air temperature had occurred in the CTMR during the last five decades. However, the detailed, comprehensive, and systematical characteristics of climate warming, such as its temporal and spatial heterogeneity, remain unclear. In this study, the temporal and spatial heterogeneity of climate warming across the CTMR had been comprehensively analyzed based on the 10-day air temperature data gathered during 1961–2020 from 26 meteorological stations. The results revealed local cooling in the context of general warming in the CTMR. The amplitude of variation (AV) varied from −0.57 to 3.64 °C, with the average value of 1.19 °C during the last six decades. The lapse rates of the elevation-dependent warming that existed annually, and in spring, summer, and autumn are −0.5 °C/100 m, −0.5 °C/100 m, −0.7 °C/100 m, and −0.4 °C/100 m, respectively. The warming in the CTMR is characteristic of high temporal heterogeneity, as represented by the amplified warming at 10-d scale for more than half a year, and the values of AV were higher than 1.09 °C of the global warming during 2011–2020 (GWV_2011–2020). Meanwhile, the amplitudes of warming differed greatly on a seasonal scale, with the rates in spring, autumn, and winter higher than that in summer. The large spatial heterogeneity of climate warming also occurred across the CTMR. The warming pole existed in the warm part, the Turpan-Hami basin (below 1000 m asl) where the air temperature itself was high. That is, the warm places were warmer across the CTMR. The cooling pole was also found in the Kuqa region (about 1000 m asl). This study could greatly improve the understanding of the spatio-temporal dynamics, patterns, and regional heterogeneity of climate warming across the CTMR and even northwest China.

The acceleration of climate warming has led to a faster solid–liquid water cycle and a decrease in solid water storage in cold regions of the Earth. Although snowfall is the most critical input for the cryosphere, the phenology of snowfall, or potential snowfall phenology (PSP), has not been thoroughly studied, and there is a lack of indicators for PSP. For this reason, we have proposed three innovative indicators, namely, the start of potential snowfall season (SPSS), the end of potential snowfall season (EPSS), and the length of potential snowfall season (LPSS), to characterize the PSP. We then explored the spatial–temporal variation in all three PSP indicators in the past, present, and future across the Chinese Tianshan mountainous region (CTMR) based on the observed daily air temperature from 26 meteorological stations during 1961–2017/2020 combined with data from 14 models from CMIP6 (Phase 6 of the Coupled Model Intercomparison Project) under four different scenarios (SSP126, SSP245, SSP370, and SSP585, where SSP represents Shared Socioeconomic Pathway) during 2021–2100. The study showed that the SPSS, EPSS, and LPSS indicators could accurately describe the PSP characteristics across the study area. In the past and present, the potential snowfall season started on 2 November, ended on 18 March, and lasted for about 4.5 months across the CTMR on average. During 1961–2017/2020, the rate of advancing the EPSS (−1.6 d per decade) was faster than that of postponing the SPSS (1.2 d per decade). It was also found that there was a significant delay in the starting time (2–13 d) and advancement in the ending time (1–13 d), respectively, resulting in a reduction of 3–26 d for the LPSS. The potential snowfall season started earlier, ended later, and lasted longer in the north and center compared with the south. Similarly, the SPSS, EPSS, and LPSS indicators are also expected to vary under the four emission scenarios during 2021–2100. Under the highest emission scenario, SSP585, the starting time is expected to be postponed by up to 41 d, while the ending time is expected to be advanced by up to 23 d across the study area. This change is expected to reduce the length of the potential snowfall season by up to 61 d (about 2 months), and the length of the potential snowfall season will only last 2.5 months in the 2100s under the SSP585 scenario. The length of the potential snowfall season in the west and southwest of the CTMR will be compressed by more days due to a more delayed starting time and an advanced ending time under all four scenarios. This suggests that, with constant snowfall intensity, annual total snowfall may decrease, including the amount and frequency, leading to a reduction in snow cover or mass, which will ultimately contribute to more rapid warming through the lower reflectivity to solar radiation. This research provides new insights into capturing the potential snowfall phenology in the alpine region and can be easily extended to other snow-dominated areas worldwide. It can also help inform snowfall monitoring and early warning for solid water resources.

Water use efficiency of vegetation (WUE), the ratio of carbon gain to water loss, is a valid indicator to describe the photosynthetic carbon–water coupling relationship. Understanding how and why WUE changes are essential for regional ecological conservation. However, the impacts of various factors and their interactions on the spatial variation of WUE remain uncertain in the arid land of Northwest China. Here, we selected the Qilian Mountains (QM) and Hexi Corridor (HC) as the study areas. Supported by the Google Earth Engine, we explored the spatio-temporal variations of WUE in QM and HC for 2002–2021 using STL decomposition (a seasonal-trend decomposition procedure), trend analysis, and the Hurst index. Then, the Geodetector method was applied to quantify impacts of geographical and eco-meteorological factors on the spatial variation of WUE. The WUE in HC was higher than that in QM. Interestingly, the opposite longitude zonality characteristics are shown in the QM and HC. In QM, the WUE showed an upward trend with longitude increasing, while a downward trend with longitude increasing in the oases of HC. The WUE of cropland was the highest (1.15 ± 0.35 gC kg⁻¹ H₂O), and that of alpine vegetation was the lowest (0.2 ± 0.15 gC kg⁻¹ H₂O). WUE showed a decreasing trend across the study area, almost certainly due to a drop from May to July during 2002–2021. The air temperature is the dominant factor influencing the spatial variation of WUE. In addition, the interaction of any two factors is greater than the independent influence of either factor alone. The Geodetector method proved to be effective for quantifying the impact of complex multi-factors on the spatial variation of WUE. This study provides a new technical scheme to analyze the spatio-temporal pattern and quantify the impact of multi-factors on the spatial variation of WUE. These findings aid in understanding underlying mechanisms of WUE variation and thereby will be beneficial for clarifying the response of vegetation to climate change.