Ground surface temperature (GST) is an essential boundary condition for permafrost modeling and satellite-product evaluation, yet high-frequency, multi-year GST observations on the Qinghai-Tibet Plateau (QTP) remain sparse, particularly within the interior continuous permafrost zone. Here we present a publicly available dataset of half-hourly GST measured at 24 sites in the Wudaoliang region of the central QTP. The network spans three dominant alpine land-cover types (alpine desert, alpine steppe, and alpine meadow) and elevations of 4,543-4,741 m a.s.l. HOBO temperature loggers were installed at 3-5 cm depth and recorded GST at 30-minute intervals from September 2019 to September 2023. The time series were quality controlled using physical-range screening and change-point checks, and disturbance-affected segments associated with frost-jacking were removed, yielding 1,399,008 valid records. The release includes quality-controlled GST time series, site-level metadata and a traceable exclusion log. This dataset supports reuse for the site- and grid-scale evaluation and calibration of satellite land surface temperature and freeze-thaw products, as well as studies of near-surface ground thermal dynamics in high-elevation permafrost regions.

Surface heat sources play a critical role in shaping meteorological conditions and permafrost dynamics on the Tibetan Plateau. To better understand surface heat source variability in the permafrost region, multiyear observational data from an isolated permafrost site and a continuous permafrost site were used to analyse the variability. The results indicated that the surface heat source at the isolated permafrost site remained relatively stable, whereas that at the continuous permafrost site increased significantly, at a rate of approximately 2.2 Wm−2yr−1. The proportions of sensible and latent heat fluxes in the surface heat source budget varied with the season. Overall, the proportion of latent heat flux was greater than that of sensible heat flux in summer and autumn, whereas the proportion of sensible heat flux was greater than that of latent heat flux in winter and spring. The peak proportion for both fluxes exceeded 80%. A random forest model effectively captured the variations in the surface heat source. And the machine learning simulation indicated that soil temperature, downward shortwave radiation and albedo were identified as major contributors to the surface heat source, collectively accounting for more than 88% of the overall variability at both sites. The impacts of snow cover on the surface heat source varied with intensity. The increasing trend of the surface heat source was closely related to climate warming in autumn and winter. A significant but weak positive correlation was observed between vegetation and the surface heat source.

Abstract. Active layer moisture (ALM) plays a fundamental role in regulating the hydrothermal dynamics, freeze–thaw processes, carbon cycling, and ecosystem and load-bearing functions of permafrost. However, spatially continuous, high-resolution datasets that characterize soil moisture across the entire active layer remain largely unavailable for the Qinghai–Tibet Plateau (QTP), which hosts the largest low- to mid-latitude permafrost region globally. Here, we present the first 90 m resolution, spatially complete dataset of ALM – the depth-averaged volumetric water content of the entire active layer – for the entire permafrost region of the QTP. The dataset was produced by integrating 342 in situ samples collected during peak-thaw seasons from 2009 to 2024 with multi-source remote sensing environmental predictors, topographic factors, and soil physicochemical properties. Four ensemble machine learning models, include Random Forest, Extra Trees, XGBoost, and CatBoost, were trained, and a bias-aware multi-model fusion strategy was applied to generate the final ALM product. SHAP-based recursive feature elimination was used to identify the dominant environmental controls and optimize feature selection. Random five-fold cross-validation showed high apparent accuracy (R² =0.62–0.63, RMSE ≈ 0.08 m³ m⁻³), and a group-based spatial cross-validation provided a conservative transferability estimate (pooled R² = 0.30–0.38). Within the permafrost region, ALM ranges from 0.02 to 0.70 m³ m⁻³, with a mean of 0.21 m³ m⁻³ and a standard deviation of 0.09 m³ m⁻³, exhibiting a distinct southeast-to-northwest decreasing gradient. The dataset is freely available with DOI: https://doi.org/10.12072/ncdc.permafrost.db7312.2026. The dataset is accompanied by an area-of-applicability (AOA) mask delineating the region – approximately 60 % of the permafrost region – where the cross-validated performance can be expected to hold, together with per-pixel uncertainty estimates derived from the DI–RMSE relationship and the divergence among the four models.

Warming permafrost is driving widespread terrain destabilization and collapse through retrogressive thaw slumps, stripping vegetation and releasing soil carbon. Despite increasing thaw slump disturbances in permafrost regions, the time and patterns of vegetation recovery remain uncertain. Here we estimate surface greenness recovery times and compositional changes following disturbances across northern tundra regions, using data from remote sensing imagery. Our findings reveal that low-stature vegetation recolonizes barren terrain in low-Arctic sites within a decade, followed by erect shrubs, resulting in greener surface than undisturbed areas. In contrast, vegetation recovery in high-Arctic and high-elevation sites requires over 30 years. Greenness recovery time (tau, years) varies widely but can be accurately predicted by a power-law function (1.35 & times; (GPP)(-1.68), P < 0.05) based on solar-induced chlorophyll fluorescence-derived ecosystem gross primary productivity (GPP, kgC m(-2) yr(-1)). We present a regionally scalable framework to quantify surface greenness recovery times and reveal divergent vegetation succession pathways following permafrost disturbances across tundra regions.

Rapid lake expansion and sudden outburst events have emerged as critical hydrological phenomena on the Qinghai–Tibet Plateau (QTP), reshaping endorheic basins and threatening downstream ecosystems and communities. However, the mechanisms and impacts of large endorheic lake failures remain poorly understood, as most studies have focused on glacial lake outbursts. The catastrophic outburst of Aksu Kule Lake (AKL) in the Kunlun Mountains in September 2024 provided a rare opportunity to investigate how prolonged hydrological accumulation in endorheic lakes on the QTP under climate change can trigger basin reorganization and flood disasters. In this study, multisource optical remote sensing data were integrated with ICESat and CryoSat-2 satellite altimetry data to systematically assess changes in lake surface area and water level and to examine the underlying mechanisms driving rapid expansion and eventual catastrophic outbursts. The rapid expansion and subsequent outburst of AKL were driven primarily by increased precipitation, especially extreme short-term events in 2010 and 2016. These events contributed approximately 1.5 × 107 m3 and 7.4 × 107 m3 of additional lake water, respectively, accounting for 13.85% and 68.08%, respectively, of the total increase in net volume from 2009 to 2023. The final outburst was initiated by overtopping and subsequent erosion of unconsolidated alluvial fan sediments at the lake outlet, rather than by the structural failure of a natural dam, underscoring the inherent vulnerability of alluvial-dammed lakes. Following the AKL outburst, the Endere River Basin expanded by 81.4%, and the original hydrological regulation capacity of the lake was compromised, potentially resulting in an increased frequency and magnitude of floods in this basin. These findings enhance understanding of rapid lake expansion and outburst mechanisms and provide a scientific basis for early-warning systems and adaptive water management strategies in endorheic basins of the QTP under climate warming.

Permafrost degradation poses a significant threat to the organic carbon (C) pool primarily through regulating microorganisms. However, microbial responses and their associations with C loss across vertical profiles remain unclear. Here, we use metagenomic sequencing to investigate bacterial communities in 125 samples from five 15 m-depth permafrost cores, spanning from the active layer to the permafrost layer along a degradation gradient on the Qinghai-Tibet Plateau. We find that α-diversity decreases, while stochastic processes and community stability increase from the active layer to the permafrost layer. Along permafrost degradation, these community attributes follow similar variations within the active layer but remain constant within the permafrost layer. The relative abundance and interaction of core taxa play important roles in maintaining community stability in the active and permafrost layers, respectively. As permafrost degrades, the negative relationships between community stability and C storage become more intense, especially in the active layer. These findings demonstrate that degradation induces microbial responses that potentially amplify C release, supporting a positive feedback loop to climate warming. Our work provides novel insights into the vertical heterogeneity of this mechanism and is crucial for modeling future permafrost C dynamics.

Changes in soil organic carbon (SOC), total nitrogen (TN), and metal elements (MEs) in permafrost regions may trigger climate feedback and human health risks under a warming climate. However, vertical distribution patterns of SOC, TN, and MEs in the active layer and deep permafrost remain unclear. Here, we collected three 0-600 cm soil cores from the central Qinghai-Tibet Plateau (QTP). The results showed that SOC and TN contents were significantly higher in the active layer than in the permafrost (p < 0.05), and MEs exhibited considerable fluctuations between the two layers. In the active layer, soil pH was the strongest predictor for SOC, TN, and MEs (p < 0.001), whereas in the permafrost, an unexpectedly strong coupling among SOC, TN, and MEs was observed (p < 0.001). Furthermore, most MEs posed a relatively low ecological risk, whereas cadmium (Cd) exhibited a moderate ecological risk at the threshold level. These findings provide novel insights for further exploring the deep biogeochemical processes of the permafrost ecosystem.

Extensive ground ice is a defining feature of permafrost regions. Climate warming degrades permafrost by thawing subsurface ice, altering water and heat transfer, and triggering ground subsidence, thermokarst development, and infrastructure instability. Accurately simulating ground ice dynamics and the resulting frost heave and thaw settlement is therefore a central challenge in predicting permafrost degradation and its environmental and hydrological consequences. Although numerical models have made substantial progress in representing coupled thermo-hydro-mechanical processes in frozen ground, considerable discrepancies persist in their accuracy and predictive performance. These discrepancies largely arise from the diverse and often over-simplified ways used to parameterize ground ice, including its initial abundance, phase-change behaviour, moisture redistribution, excess-ice thaw, segregated-ice formation, and contribution to surface deformation. This study presents a systematic review of model representations and parameterization schemes for ground ice processes, highlighting their importance for improving the performance and reliability of permafrost models. It first summarizes how ground ice processes are represented in numerical models, with particular emphasis on different treatments of ground ice formation and degradation mechanisms. It then synthesizes existing model-based simulations of surface deformation associated with ground ice changes. Finally, the review identifies key limitations in current approaches and outlines priorities for future model development, including improved parameterization of ground ice, integration of multi-source observational data, advances in parameter inversion techniques, enhanced thermo-hydro-mechanical coupling, and the application of artificial intelligence for automated parameter calibration. The overarching aim is to strengthen the theoretical foundation for understanding permafrost dynamics and to improve the reliability of model-based predictions.

Soil hydrothermal processes in permafrost regions are critical for land-atmosphere exchange but are challenging to simulate accurately in models, largely due to the parameterization of unfrozen water content. This study evaluated 11 freezing-point depression schemes, derived from combinations of three soil water characteristic curves (SWCCs) and four soil matric potential schemes, using CLM5.0 at six sites across the Arctic and Qinghai-Tibet Plateau (QTP). Results showed regionally dependent optimal schemes. For soil temperature, a combined effective porosity and cryosuction scheme (TEST3) reduced the RMSE by 0.51-0.52 degrees C (7.1-8.3%) in the Arctic, while a cryosuction scheme (TEST10) was best on the QTP, reducing RMSE by 0.04-0.06 degrees C. For soil moisture, a Van Genuchten SWCC scheme with effective porosity (TEST5) reduced RMSE by up to 0.018 m 3 /m 3 (13.2%) in the Arctic, and TEST4/TEST5 performed best on the QTP. The explicit parameterization of residual water content in Brooks & Corey and Van Genuchten SWCCs was a key mechanism, correcting the default scheme's large soil moisture bias by up to 53% during freezing. Cryosuction increased unfrozen water, while effective porosity decreased it. However, model structural limitations caused unreliable matric potential output during freezing. Persistent biases at specific sites were attributed to inaccurate soil texture data, unaccounted lateral flow, and insufficient snow insulation representation. This study highlights the regional applicability of schemes and provides critical insights for improving permafrost simulations.

Arctic permafrost is experiencing unprecedented degradation, and the resulting thaw settlement hazards pose severe threats to the life and infrastructure of local people. However, a gap remains in our comprehensive understanding of the controlling factors and training sample selection strategies. Combining GeoDetector with machine learning models, we conducted a comprehensive evaluation based on 12 factors, including topographic, edaphic, vegetation cover, and climatic properties. The results revealed that among the factors inducing thaw settlement events, the interaction between ground ice content (GIC) and thawing degree day (TDD) played the most critical role (q = 0.71), whereas GIC alone demonstrated moderate explanatory power (q = 0.39). This finding indicated, to a certain extent, that the presence of ground ice was a necessary condition, rather than a deterministic factor, for thaw settlement in permafrost regions. The thaw settlement susceptibility map produced by the optimal machine learning model implied that more than 40% of the circum-Arctic permafrost was in high- and very high-susceptibility regions, with a relatively small proportion (13.81%) in medium-susceptibility regions. The hazard risk assessment results revealed that, 2.71 million people and buildings covering 204 km2 and 110,312 km of roads are currently under the threat of varying risk levels. Notably, the proportions of high-risk (including very high-risk) regions were 73.34%, 75.74%, and 64.8%, respectively. High-risk regions clearly exhibited a high intensity of human activity. Our results demonstrated a notably increased threat of thaw settlement in the circum-Arctic permafrost region compared with previous research.

Permafrost degradation is accelerating worldwide as climate warming intensifies, and air temperature is widely recognized as the dominant driver of permafrost thermal changes. In contrast, the role of rainfall, which exhibits strong temporal and spatial variability, remains poorly quantified. In this study, a permafrost-adapted land surface model to was used to quantify the effects of summer light and moderate rainfall on permafrost thermal regimes across the Qinghai-Tibet Plateau. Light and moderate rainfall each contribute approximately 40 % of summer precipitation but display distinct spatial distributions, with light rainfall prevailing dry regions and moderate rainfall dominating wetter areas. Simulation results indicate that increases in both rainfall types enhance latent heat flux while suppressing sensible and ground heat fluxes, thereby reducing net soil heat input and inducing widespread cooling within the active layer and near-surface permafrost. Cooling is most pronounced in dry areas and within the upper similar to 3 m of the soil profile. For equivalent precipitation increases, light rainfall produces a stronger cooling response than moderate rainfall. Specifically, a 10 mm increase in light rainfall reduces active layer thickness and temperature at the top of permafrost by 0.05 m and 0.03 degrees C, respectively, compared with corresponding reductions of 0.04 m and 0.02 degrees C for moderate rainfall. Derived from a controlled sensitivity experiment designed to isolate rainfall effects, these results highlight the importance of explicitly accounting for rainfall type, intensity, and spatial variability in permafrost assessments. The findings further demonstrate that frequent light rainfall can partially offset permafrost warming in arid alpine environments.

Soil thermal conductivity (STC) governs near-surface heat exchange and constrains simulations of active-layer evolution and permafrost change. Using a 10-year record from four Qinghai-Tibet Plateau sites (0-10 cm), laboratory Kersten number (K-e)-saturation (S-r) calibrations, and a structure-aware Johansen implementation, we identify a moisture-threshold reversal: under low antecedent moisture the frozen state conducts less heat than the unfrozen state, while at higher moisture the conventional ordering returns. The crossover saturation S-r* is traceable in calibrated K-e-S-r relations and observable from pre-freeze moisture, linking field diagnosis to model parameters. A compact, deployable correction follows: taper the frozen branch for S-r < S-r*, compute endmembers from measured bulk density, porosity, and quartz fraction (BD-n-q), and select the unfrozen K-e(S-r) form by soil class and dryness tendency. The scheme reduces unfrozen-season errors across the core sites and generalizes at an independent hold-out station (TGL) without site-specific tuning. The approach is transparent-inputs are observable and decisions are tied to S-r*-and is most impactful in dry, coarse, and sparsely monitored regions.

The urban artificial lakes play an important role in regional water balance and resource management in urban ecosystems, and the evaporative water loss of these urban lakes is sensitively responsive to environmental change. Stable hydrogen and oxygen isotopes are natural tracers of the water cycle, enabling the quantification of the lake water budget. Here, we collected 625 lake water samples from three urban artificial lakes in a semi-arid city on the western Chinese Loess Plateau and estimated the evaporative water loss based on daily measurements of stable water isotopes. Results indicate that the slopes of the local lake evaporation lines for three urban lakes ranged between 4 and 6, reflecting an arid climate background. The urban lakes show spatial incoherence in the evaporation to inflow ratio (E/I), with the annual averages ranging from 20.3% to 40.5% among the three lakes. Deuterium excess (d-excess) showed a significant inverse relationship with E/I, confirming a decrease in deuterium excess during evaporation. Sensitivity analysis indicates that lake water isotope measurements play a critical role in lake evaporation models, with relative humidity exerting a significant influence. Furthermore, utilizing measured atmospheric water vapor isotope data can improve the accuracy of E/I estimates, whereas simplified methods based on isotopic equilibrium fractionation assumptions still exhibit bias. The emerging artificial urban lakes in past decades may enhance moisture recycling in an arid setting. This study elucidates the influencing factors of evaporation fractionation in urban lakes in a semi-arid climate, which is useful for understanding spatial incoherence in urban lake evaporation and enhancing urban hydrological modeling and water resource management.

Accurate soil thermal conductivity (STC) data and their spatiotemporal variability are critical for the accurate simulation of future changes in Arctic permafrost. However, in-situ measured STC data remain scarce in the Arctic permafrost region, and the STC parameterization schemes commonly used in current land surface process models (LSMs) fail to meet the actual needs of accurate simulation of hydrothermal processes in permafrost, leading to considerable errors in the simulation results of Arctic permafrost. This study used the XGBoost method to simulate the spatial-temporal variability of the STC in the upper 5 cm active layer of Arctic permafrost during thawing and freezing periods from 1980 to 2020. The findings indicated STC variations between the thawing and freezing periods across different years, with values ranging from-0.4 to 0.28 W & sdot;m-1 & sdot;K-1. The mean STC during the freezing period was higher than that during the thawing period. Tundra, forest, and barren land exhibited the greatest sensitivity of STC to freeze-thaw transitions. This is the first study to explore the long-term spatiotemporal variations of STC in Arctic permafrost, and these findings and datasets can provide useful support for future research on Arctic permafrost evolution simulations.

Mongolia has experienced increasingly extreme climate events in recent years. Most previous studies have used data from meteorological stations with uneven spatial coverage and inconsistent time series, resulting in an insufficient understanding of regional variations in extreme climate events. In particular, the characteristics of extreme climate events in the two relatively warm years of 2022 and 2023 need to be investigated. Analysis using the ERA5-Land reanalysis data revealed that the hottest day (TXx), coldest night (TNn), coldest day (TXn), extreme warm days (TX90p), summer days (SU25) and the warm spell duration indicator (WSDI) presented increasing trends, whereas the extreme cold nights (TN10p), frost days (FD0) and the cold spell duration indicator (CSDI) presented decreasing trends during 1961 to 2023 in Mongolia. Moreover, the increasing rates of the TXx, TNn, TX90p and WSDI in the permafrost regions (0.042 degrees C/year, 0.049 degrees C/year, 0.244%/year and 0.349 days/year, respectively) were greater than those in the seasonally frozen ground regions (0.031 degrees C/year, 0.044 degrees C/year, 0.223%/year and 0.312 days/year, respectively), indicating that the extreme temperature indices in the permafrost regions were more sensitive to extreme warm events. All extreme precipitation indices showed decreasing trends, with higher values in northern Mongolia, except for consecutive dry days (CDD). In 2022 and 2023, the mean annual air temperature (MAAT), TXx, TX90p, SU25 and WSDI accounted for more than 50% of the TOP_10, and CDD accounted for more than 20%, reflecting more extreme warm events and droughts. Compared with neighbouring regions, Mongolia experienced faster increases in extreme warm events and droughts. The Asian Polar Vortex Intensity Index (AAI), Atlantic Multidecadal Oscillation Index (AMO) and MAAT have the greatest impact on extreme temperature indices, and the annual total precipitation (ATP) has the greatest impact on extreme precipitation indices. These findings help study the hydrological, ecological and social impacts of extreme climate events.

Study region: The study is conducted in permafrost regions of the Qinghai-Tibet Plateau (QTP), with validations carried out at three in situ sites that have different underlying surfaces and a test region spanning from 32.5 degrees to 36 degrees N and from 88 degrees to 96 degrees E. Study focus: The study focuses on evaluating a physical method, the half-order derivative (HOD), for estimating the ground soil heat flux (G0) in permafrost regions. It also examines the applicability of using land surface temperature (LST) as a substitute for ground surface temperature (GST) and introduces modifications to the soil thermal inertia (Gamma), start time of integration (Sti), and phase shift time (Pst) to improve accuracy of the HOD method. New hydrological insights for the region: The modified HOD method demonstrates high accuracy and robustness for estimating G0. It reduces the normalized mean error (NME) by at least 135.8 %/45.3 % (daily/30-min scale) during the frozen period and 86.7 %/25.8 % during the thawed period when the LST data are used as inputs. Furthermore, the modified HOD method outperforms empirical methods, reducing the NME by at least 25.5 %/3.6 % during the frozen period and 14.6 %/0.5 % during the thawed period. Additionally, the method's ability to utilize satellite-derived LST data makes it a promising tool for improving the estimation of G0 on diurnal and daily scales, with potential for integration into remote sensing surface energy balance (SEB) models for regional applications in the QTP.

Plant- and microbial-derived compounds has been recognized as key contributors to vulnerable and stable soil organic carbon (SOC) pools. However, the relative contributions of these sources along altitudinal gradients remain unclear. This study quantified the contributions of plant- and microbial-derived carbon (C) to SOC across four distinct vegetation zones along an altitudinal gradient ranging from 2600 to 3670 m in northwest China. SOC content increased significantly along altitudinal gradients in the 0–20 cm and 20–40 cm, indicating greater C sequestration in higher elevations. Both plant lignin and microbial necromass also increased with altitude in both soil layers. Notably, in lower altitudes, SOC accumulation was predominantly driven by plant-derived C, while in higher altitudes, microbial-derived C was dominated. The substantial SOC storage in higher altitudes is more microbially processed, which contributes to greater SOC stability, as opposed to the lower altitudes, where SOC is less stable and more vulnerable to environmental change. Regression analysis and random forest modeling reveal that soil pH, moisture, and total nitrogen as the primary regulators of plant lignin and microbial necromass, surpassing the influence of plant inputs such as root biomass. In conclusion, the content of both plant lignin and microbial necromass increases with altitude, while their respective contributions to SOC follow divergent patterns. These findings have significant implications for predicting C loss as a result of global climate change, underscoring the need for targeted conservation strategies across different altitudinal zones.

As the climate continues to warm, the thawing of ice-rich permafrost leads to changes in the polygonal patterned ground (PPG) landscape, exhibiting an array of spatial heterogeneity in trough patterns, governing permafrost stability and hydrological and ecosystem dynamics. Developing accurate methods for detecting trough areas will allow us to better understand where the degradation of PPG occurs. The Geomorphon approach is proven to be a computationally efficient method that utilizes digital elevation models (DEMs) for terrain classification across multiple scales. In this study, we firstly evaluate the appliance of the Geomorphon algorithm in trough mapping in Prudhoe Bay (PB) in Alaska and the Wudaoliang region (WDL) on the central Qinghai–Tibet Plateau. We used the optimized DEM resolution, flatness threshold (t), and search radius (L) as input parameters for Geomorphon. The accuracy of trough recognition was evaluated against that of hand-digitized troughs and field measurements, using the mean intersection over union (mIOU) and the F1 Score. By setting a classification threshold, the troughs were detected where the Geomorphon values were larger than 6. The results show that (i) the lowest t value (0°) captured the microtopograhy of the troughs, while the larger L values paired with a DEM resolution of 50 cm diminished the impact of minor noise, improving the accuracy of trough detection; (ii) the optimized Geomorphon model produced trough maps with a high accuracy, achieving mIOU and F1 Scores of 0.89 and 0.90 in PB and 0.84 and 0.87 in WDL, respectively; and (iii) compared with the polygonal boundaries, the trough maps can derive the heterogeneous features to quantify the degradation of PPG. By comparing with the traditional terrain indices for trough classification, Geomorphon provides a direct classification of troughs, thus advancing the scientific reproducibility of comparisons in PB and WDL. This work provides a valuable method that may propel future pan-Arctic studies of trough mapping.

As a natural tracer of the water cycle, the stable water isotopes in precipitation usually show spatial dependency. The geographical zoning of precipitation isotopes provides a spatial perspective to understand the large-scale climatological background associated with isotopic fractionation. Here a Ward hierarchical clustering analysis was conducted on a monthly delta 18O product across China with a spatial resolution of 10 ' x 10 ' (latitude by longitude), and a new zoning scheme for precipitation isotopes in China was designed. According to the intraannual isotopic variations, the terrestrial area of China was divided into seven subregions in this new scheme, i.e., Northeast China (NE), North China (N), South China (S), arid Northwest China (NWa), extreme arid Northwest China (NWea), cold Southwest China (SWc), and humid Southwest China (SWh). We examined the monthly variations in precipitation isotopes and local meteoric water lines for each subregion. The subregional slopes of local meteoric water lines range between 6.52 (Northeast China) and 8.20 (South China). According to the climate reanalysis data, we quantified the input and output of atmospheric water vapor from the meridional and zonal boundaries for each subregion, and found that the regional isotopic characteristics are consistent with the water vapor budgets. The scheme reveals the large-scale moisture transport of monsoon systems and the topographic effect of plateaus and basins. The new zoning scheme in this study provides fundamental isotopic information for diverse climate backgrounds in China, and is useful for regional studies in the atmospheric, hydrological and ecological fields.