General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

Based on three IPCC (Intergovernmental Panel on Climate Change) Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), observed meteorological data, ERA-40 reanalysis data, and five preferred GCM (general circulation model) outputs selected from 23 GCMs of CMIP5 (Phase 5 of the Coupled Model Intercomparison Project), climate change scenarios including daily precipitation, maximum air temperature, and minimum air temperature from 2021 to 2050 in the Heihe River basin, which is the second largest inland river basin in Northwest China, were generated by constructing a statistical downscaling model (SDSM). Results showed that the SDSM had a good prediction capacity for the air temperature in the Heihe River basin. During the calibration and validation periods from 1961 to 1990 and from 1991 to 2000, respectively, the coefficient of determination (R2) and the Nash–Sutcliffe efficiency coefficient (NSE) were both larger than 0.9, while the root mean square error (RMSE) was within 20%. However, the SDSM showed a relative lower simulation efficiency for precipitation, with R2 and NSE values of most meteorological stations reaching 0.5, except for stations located in the downstream desert areas. Compared with the baseline period (1976–2005), changes in the annual mean precipitation simulated by different GCMs during 2021–2050 showed great difference in the three RCP scenarios, fluctuating from −10 to +10%, which became much more significant at seasonal and monthly time scales, except for the consistent decreasing trend in summer and increasing trend in spring. However, the maximum and minimum air temperature exhibited a similar increasing tendency during 2021–2050 in all RCP scenarios, with a higher increase in maximum air temperature, which increased as the CO2 concentration of the RCP scenarios increased. The results could provide scientific reference for sustainable agricultural production and water resources management in arid inland areas subject to climate change.

The dry-wet transition is of great importance for vegetation dynamics, however the response mechanism of vegetation variations is still unclear due to the complicated effects of climate change. As a critical ecologically fragile area located in the southeast Qinghai-Tibet Plateau, the Yarlung Zangbo River (YZR) basin, which was selected as the typical area in this study, is significantly sensitive and vulnerable to climate change. The standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) based on the GLDAS-NOAH products and the GIMMS-NDVI remote sensing data from 1982 to 2015 were employed to investigate the spatio-temporal characteristics of the dry-wet regime and the vegetation dynamic responses. The results showed that: (1) The spatio-temporal patterns of the precipitation and temperature simulated by the GLDAS-NOAH fitted well with those of the in-situ data. (2) During the period of 1982–2015, the whole YZR basin exhibited an overall wetting tendency. However, the spatio-temporal characteristics of the dry-wet regime exhibited a reversal phenomenon before and after 2000, which was jointly identified by the SPEI and runoff. That is, the YZR basin showed a wetting trend before 2000 and a drying trend after 2000; the arid areas in the basin showed a tendency of wetting whereas the humid areas exhibited a trend of drying. (3) The region where NDVI was positively correlated with SPEI accounted for approximately 70% of the basin area, demonstrating a similar spatio-temporal reversal phenomenon of the vegetation around 2000, indicating that the dry-wet condition is of great importance for the evolution of vegetation. (4) The SPEI showed a much more significant positive correlation with the soil water content which accounted for more than 95% of the basin area, implying that the soil water content was an important indicator to identify the dry-wet transition in the YZR basin.

Soil moisture (SM) is a key factor in the exchanging process of the hydrological cycle, which is rather difficult to be directly observed. In situ measurements on SM, however, are subjected to the point scales. Use of land surface models has been a promising way to explore SM variations, especially for poorly gauged high mountain regions such as the Yarlung Zangbo River (YZR) basin located in Southeast Tibetan Plateau. This study made an attempt to investigate the spatiotemporal variations of SM and discuss hydro‐meteorological factors impacting SM evolution based on the Global Land Data Assimilation Systems (GLDAS) outputs during the period 1970–2009. Results show that (a) GLDAS data sets have high agreement and low bias with in situ measurements and consistent spatial distribution with ERA reanalysis data sets; (b) an abrupt change of SM is detected in 1992 and a significantly decreasing trend happens during 1970–2009 and 1993–2009; (c) precipitation is the dominant climatic factor controlling SM during the period 1970–2009, whereas surface air temperature is the critical factor for the significant change of SM. Owing to the significant increasing of surface air temperature since 1992, its impact on SM increased by ∼91% than that before 1992. Evapotranspiration (ET) and snow water equivalent (SWE) are also taken into consideration, showing relatively weak influence on SM, which may be due to the dynamic process of coupled SM‐ET or low snow cover area fraction across the YZR basin. Findings in this study have important implications for SM variations in poorly gauged high mountain regions which may largely influences downstream water availability.

Reliable partitioning of precipitation (P) into runoff (Q) and evapotranspiration (E a ) is crucial for hydrological research and application, especially for regions with scarce data (Yang et al., 2007;Zhang et al., 2018). Understanding the controls of catchment properties on hydrological partitioning helps achieve reliable hydrology partitioning, but remains a challenging task (Sinha et al., 2020). The Budyko framework has been widely used to establish the relationship between evaporative ratio (i.e., E a /P) and relative availability of water and energy (i.e., aridity index (AI), the ratio between long-term E P and P, AI = E p /P) (Budyko, 1974;Cheng et al., 2011). In the widely used Budyko framework proposed by Fu (1981) (i.e., Fu's equation), the controls of other secondary factors on partitioning are lumped into a landscape parameter (ω) that includes intra-annual climate variability, soil, vegetation, and topography (Cheng et al., 2021;Fu, 1981;Zhang et al., 2004). Note that Fu's equation is further explained in Section 2.1. Parameterizing ω with catchment properties not only improves the simulation accuracy of Fu's equation (Greve et al., 2015), but also reveals the controls of climate, physiography, and vegetation on hydrological partitioning (Abatzoglou & Ficklin, 2017). However, current understanding of controls on hydrological partitioning are still very limited, and building a physically-based relationship between ω and the control factors is difficult due to the complex (nonlinear) interactions between climate and catchment processes (