ABSTRACT Blocking highs over Eastern Europe (EEU) have been identified as significant drivers of the extreme precipitation events over Pakistan in 2010 and 2022. However, whether this insight is applicable over longer periods remains unclear. This study, on the basis of observations and reanalysis data from 1961 to 2022, statistical analysis and numerical simulations, reveals that the impact of EEU blocking high on precipitation in Pakistan has intensified after the 1990s and attributes this shift to the warming trend in the Arctic. In recent extreme rainfall cases, EEU blocking highs transmitted Rossby wave energy southeastward, crossing the polar front jet and subtropical jet and leading to a high‐pressure anomaly over China. This effect, coupled with the anomalous activities of tropical oceanic oscillations and the South Asian monsoon, created favourable conditions for extreme rainfall in Pakistan. Arctic warming contributed to this shift by altering the westerly jet over EEU. The jet changes displaced the blocking activity centre southward from the Barents and Kara Seas, facilitating the propagation of Rossby wave trains that connected the EEU blocking highs with extreme rainfall over Pakistan. This study elucidates the mechanisms driving extreme precipitation in Pakistan and offers valuable insights for future prediction. Given the continued warming trend, similar extreme rainfall events driven by these mechanisms are likely to recur in the future. This underscores the urgent need for local governments and international agencies to take more proactive measures to mitigate potential disasters.

The Southwest China vortex (SWV) is a high-impact mesoscale cyclonic vortex that typically originates over Sichuan Province, China, and frequently produces hazardous rainfall. Yet systematic knowledge of the structural and microphysical properties of SWV precipitation remains insufficiently quantified. Using Global Precipitation Measurement Dual-frequency Precipitation Radar (GPM/DPR) observations from 2014 to 2022, this study investigates the vertical structure and macro- and microphysical characteristics of SWV precipitation, and quantifies their differences across life-cycle stages and precipitation types. The mature stage is characterized by higher echo tops, stronger radar reflectivity, higher strong-echo altitudes, and larger near-surface rainfall, together with a clearer melting-layer bright band and a stronger post-melting shift toward larger drops and lower number concentrations. The developing stage is weakest and shows the largest fraction of coalescence–breakup balance signatures, whereas the dissipating stage features enhanced evaporation- and breakup-related signals. Among precipitation types, deep strong convection exhibits the greatest vertical extent with enhanced ice/mixed-phase growth; stratiform precipitation produces stronger radar echoes and higher rainfall rates than deep weak convection despite similar echo-top heights; and shallow precipitation is characterized by smaller drops, higher concentrations, and active warm-rain spectral evolution. These findings provide satellite-based constraints for microphysics parameterization evaluation and improved numerical prediction of SWV-related rainfall over complex terrain.

Sea ice concentration (SIC) is crucial to the global climate. In this study, a new single-channel SIC retrieval algorithm utilizing spaceborne L-band brightness temperature (TB) measurements is developed based on a microwave radiative transfer model. Additionally, its four uncertainties are quantified and constrained: (1) variations in seawater reference TB under warm water conditions, (2) variations in sea ice reference TB under extremely low-temperature conditions, (3) the freeze–thaw dynamics of sea ice captured by Diurnal Amplitude Variation (DAV) signals, and (4) Land mask imperfections. It is found that DAV has the most pronounced effect: eliminating its influence reduces RMSE from 10.51% to 8.43%, increases R from 0.92 to 0.94, and minimizes Bias from -0.68 to 0.13. Suppressing all four uncertainties lowers RMSE to 7.42% (a 3% improvement). Furthermore, the algorithm exhibits robust agreement with the seasonal variability of SSM/I SIC, with R mostly exceeding 0.9, RMSE mostly below 10%, and Biases mostly within 5% throughout the year. Compared to ship-based and SAR SIC data, the new L-band algorithm’s Bias and RMSE are only 2% and 2% (ship-based)/2% and 1% (SAR) higher, respectively, than those of the SSM/I product. Future algorithms can integrate the DAV signal more effectively to better understand sea ice freeze–thaw processes and ice-atmosphere interactions.

The L-band radiative transfer-forward modeling plays a crucial role in data assimilation for meteorological forecasting. By utilizing information from the underlying surface (typically land surface parameters and variables), such as soil moisture, soil temperature, snow cover, freeze–thaw status, and vegetation, the corresponding brightness temperatures can be simulated through the physical processes described by radiative transfer models. Data assimilation becomes meaningful when the errors introduced by the simulated brightness temperatures are smaller than the simulation accuracy of the land surface variables. However, radiative transfer models at the L-band cannot accurately simulate TB operationally. In this study, four machine learning methods, including random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and deep neural networks (DNN), are employed to reconstruct the forward relationship from land surface parameters to brightness temperatures, serving as an alternative to traditional radiative transfer models. The performance of these methods is evaluated using ground-truthed soil moisture data, soil texture static data, and leaf area index (LAI). The results indicate that DNN and RF exhibit superior performance, with DNN achieving the lowest average unbiased root mean square error (ubRMSE) of 6.238 K for vertical polarization brightness temperature (TBv) and 9.033 K for horizontal polarization brightness temperature (TBh). Regarding correlation coefficients between the retrieved brightness temperatures and satellite measurements, RF leads for H-polarized TB with a value of 0.943, while both RF and SVM perform well for V-polarized TB with values of 0.930 and 0.932, respectively. In conclusion, our study shows that DNN is the optimal method for retrieving brightness temperatures, outperforming other machine learning approaches regarding error metrics and correlation with satellite measurements. These findings highlight the potential of DNN in improving data assimilation processes in meteorological forecasting.

Knowledge of raindrop size distribution (RSD) is essential for understanding microphysical processes occurring within cloud and precipitation systems, as well as for enhancing the capabilities of numerical models and radar-based quantitative precipitation estimation (QPE). However, observation and study of RSD, especially its temporal and spatial variability, remain quite limited in specific regions. One such region is Southeast China. In this paper, four years of disdrometer data from a south coastal plain site (CPS) and a north hilly inland site (HIS) in the Fujian Province of Southeast China are analyzed and compared to elucidate the characteristics and discrepancies of RSD between these two distinct climatological sites. On this basis, empirical relations between the parameters of Gamma distribution and between radar reflectivity factor (Z) and rain rate (R) are proposed. The results are summarized as follows. (1) In the cases of light to moderate rains, HIS exhibits a higher (lower) concentration of small-size (midsize and large) raindrops with diameters of D < 1 mm (1 ⩽ D < 3 and D ⩾ 3 mm), compared to CPS. Conversely, as the rain intensity increases, the raindrop concentrations across all size categories at CPS gradually exceed those at HIS. (2) RSDs at both sites broaden and exhibit elevated concentrations across most diameter categories as the rain rate increases. (3) For rainfalls with rain rates below 5 mm h−1, collision and coalescence dominate, resulting in unimodal rain spectra at both sites; whereas for stronger rainfalls, breakup intensifies, leading to the development of bimodal rain spectra. (4) HIS experiences more stratiform rains but fewer, weaker convective rains than CPS. Stratiform RSD at HIS possesses more small and large raindrops but fewer midsize raindrops compared to CPS, whereas convective RSD at CPS possesses higher concentrations across all diameter categories. (5) Accordingly, specific Z–R relations at these two sites are proposed and validated for two real cases, demonstrating that the accuracy of radar QPE is effectively improved based on the proposed Z–R relations.

This study is the first attempt to assess and enhance the applicability of exponential filter (ExpF) model to estimate rootzone soil moisture (RZSM) across different climatic and land conditions over the Tibetan Plateau (TP). To this end, in situ soil moisture (SM) profile data collected from five regional-scale monitoring networks are firstly used for the model assessment at local scale. Then a random forest (RF) approach is adopted to regionalize the critical model parameter (i.e. characteristic time length T) to enhance the estimation of RZSM at plateau scale using satellite-based surface SM (SSM) data from the Copernicus Climate Change Service (C3S). Results indicate that with increasing soil depth, the application accuracy of the ExpF model decreases due to weakened coupling strength between the SSM and RZSM, while the T values and their spatial heterogeneity increase due to greater variability in hydraulic connectivity between the SSM and RZSM across different monitoring sites. From the arid west TP to the semi-arid and humid east TP, the application accuracy of the ExpF model increases, while the variability in model accuracy across different monitoring sites shows a decreasing trend. The spatial distribution of regionalized T values at shallower depth is opposite to that of sand content, with higher values in the northwest TP and lower values in the Qaidam basin and central TP. For the deeper depth, higher values are also noted in the southeast TP due to the increasingly significant impact of meteorological and vegetation factors. The RZSM estimations based on the ExpF model with the regionalized T values capture better the observed dynamics and largely resolve the deficiencies noted for the C3S-based RZSM product. Above findings confirm the applicability of the ExpF model on the TP, offering valuable insights into enhancing the accuracy of RZSM estimations based on the satellite-based SSM products.

Previous research has primarily focused on assessing seasonal mean or annual extreme climate events, whereas intraseasonal variability in extreme climate has received comparatively little attention, despite its importance for understanding short-term climate dynamics and associated risks. This study evaluates the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing summer maximum temperature (Tmax) variability across China during 1979–2014, with the variability defined as the standard deviation of daily Tmax anomalies for each summer. Results show that most CMIP6 models fail to reproduce the observed north–south gradient of Tmax variability with significant regional biases and limited agreement on temporal trends. The multi-model ensemble (MME) outperforms most individual models in terms of root-mean-square error and spatial correlation, but it still under-represents the observed temporal trends, especially over southeastern and central China. Taylor diagram analysis reveals that EC-Earth3, GISS-E2-1-G, IPSL-CM6A-LR, and the MME perform relatively well in capturing the spatial characteristics of Tmax variability, whereas MIROC6 shows the poorest performance. These findings highlight the persistent limitations in simulating intraseasonal Tmax variability and underscore the need for improved model representations of regional climate dynamics over China.

Previous studies are more oriented toward the impacts of snow cover on seasonal mean or annual extreme climate over local and remote regions, rather than on intraseasonal variability in extreme climate. In this study, the influence of Eurasian spring snowmelt (SSD, Spring Snow water equivalent Differences) on intraseasonal extreme minimum temperature (T min) variability in May and June (MJ) over northeastern China and the relevant physical mechanisms during 1979-2018 are investigated. Results show that the dominant mode of Eurasian SSD features an east-west dipole pattern, characterised by reversed SSD anomalies over Siberia and Europe. Decreased Siberian SSD contributes to enlarged T min variability in MJ over northeastern China. The days with area-averaged T min anomalies over northeastern China greater than the 90th (90P) and less than the 10th (10P) percentiles are selected to explore the relevant mechanisms. Deficient Siberian SSD corresponds to excessive evapotranspiration and snow cover in MJ with a more pronounced increase in 10P, resulting in anomalous thermal conditions and thereby modulating downstream atmospheric circulation variability. The anomalous atmospheric circulations are conducive to increased TN90P (warm nights) and TN10P (cold nights), as well as lower TNn (minimum T min) and higher TNx (maximum T min). Thus, increased intraseasonal T min variability appears over northeastern China due to a larger T min range and more TN90P and TN10P occurrences. Moreover, SSD is a key driver for T min variability in MJ over northeastern China, and the North Atlantic sea surface temperature provides atmospheric circulations that favour the formation of SSD distributions. The present findings highlight that Eurasian SSD is an important indicator for T min variability predictability over northeastern China.

While land‐atmosphere water‐heat exchange critically influences climate variability and the water cycle, particularly in cold regions, it is inadequately comprehended due to insufficient observational data. This study aims to improve the performance of the community Noah land surface model with multiparameterization options (Noah‐MP) model in water and heat transfer simulations and explore the sensitivity of regional land‐atmosphere coupling to soil moisture over the Tibetan Plateau. The model is evaluated against data from eight eddy covariance sites, four soil temperature and moisture networks, and seven reanalysis products. Various sensibility tests are conducted, including the replacement of soil property, surface drag coefficient scheme, canopy stomatal resistance scheme, soil surface resistance scheme, and their different combinations. The results indicate that different schemes can improve certain aspects of model simulations. Specifically, the modified surface drag coefficient scheme reduces the overestimation in sensible heat flux by adjusting the surface heat exchange coefficient, while the improved stomatal and soil resistance schemes enhance latent heat flux and soil moisture simulations. The optimal combination significantly reduces average bias by 61.3% for the Bowen ratio, 9.6% for soil temperature, and 50.0% for soil moisture. Regional simulations demonstrate that sensible heat flux constitutes the primary constituent within the energy partitioning, characterized by a mean Bowen ratio of 1.84. In arid and semiarid zones, the Bowen ratios are 3.10 and 1.75, respectively, underscoring stronger surface energy exchange capacity over drier soil conditions.

The Antarctic ice sheet, Earth’s largest ice mass, is vital to the global climate system. Analyzing its thermal behavior is crucial for sea-level projections and ice shelf assessments; however, internal temperature studies remain challenging due to the harsh environment and limited access to the site. Using ten years of Soil Moisture Active Passive (SMAP) satellite passive microwave brightness temperature (TB) data (2015–2025), we examined changes in TB across Antarctica. Results show a stronger warming trend in West Antarctica, with TB increasing by over 1.5 K over a decade, while East Antarctica remains relatively stable, showing only seasonal summer warming and winter cooling. Furthermore, TB in the Antarctic region correlates best with internal temperatures at depths of 500–2000 m, as indicated by the effective soil temperature, as demonstrated by the modeling data and the τ-z model’s inference. However, the total enthalpy is inconsistent with the TB trend and exhibits the opposite effect when combined with the sensing depth. By comparing the weak trend in surface ice temperature changes, we conclude that the TB warming trend observed on the western side of the Antarctic over the past decade does not originate from the increasing temperatures within the internal ice shelves, which differs from the increase in temperatures at the Antarctic margins.

Producing reliable profile soil moisture and temperature (SMST) simulations for the Tibetan Plateau (TP) is challenging with current model-based products. This study examines error sources in GLDAS-2.1 Noah through numerical experiments focusing on impact of soil properties, meteorological forcing, and model physics. Profile SMST observations from the Maqu network characterized by grassland with humid climate and Shiquanhe network dominated by bare ground with arid climate serve as ground truth. The control experiment running the default Noah model with GLDAS-2.1 meteorological data and FAO soil data mirrors the GLDAS-2.1 Noah product, both of which underestimate profile SM in Maqu and overestimate them in Shiquanhe, with profile ST underestimated in both areas. Using realistic soil types from in situ samples reduces RMSD by 27% and 57% on average in simulating profile SM for Maqu and Shiquanhe, respectively. Adoption of improved meteorological forcing further alleviates remaining overestimation in Shiquanhe during warm season with RMSD reduced by 45%. Implementation of augmented model physics largely addresses remaining deficiencies, which further reduces RMSD by more than 40% in both network via improving parameterizations of soil hydraulic properties and freezing characteristics. Implementation of improved soil type and meteorological forcing shows minor impact on profile ST simulations, while the augmented model physics improving the parameterization of surface heat exchange largely reduces the RMSD by 34% and 51% for Maqu and Shiquanhe, respectively. These findings provide valuable insights for understanding and addressing the uncertainties of profile SMST simulations on the TP.

This study aims to investigate the microphysical structure and hydrometeor conversion processes of convective clouds in the Yushu region of the Tibetan Plateau （referred to as the Plateau）.Using the WRF mesoscale numerical forecast model combined with observational data from the Yushu region in Qinghai during the summer of 2019， we analyzed a summer convective precipitation event in the Yushu area.The results show： （1） The 24-hour cumulative precipitation simulated by WRF is similar to the observed precipitation at the Yushu station.The spatial and temporal distribution of simulated precipitation echoes is generally consistent with Ka-band millimeter-wave cloud radar detection results， indicating the reliability of the simulation results.（2） Particles of different phases in precipitation clouds show distinct vertical distribution structures.The maximum centers of solid hydrometeors are all at relatively high altitudes， with cloud ice's maximum center being the highest at around 200 hPa.The maximum center of liquid hydrometeors is at 500 hPa.Water vapor's maximum center is at the lowest height， below 500 hPa， and its maximum value appears earlier than other particles.（3） In cloud microphysical conversion processes， cloud water makes the largest contribution to precipitation.Water vapor forms snow， graupel， and other hydrometeors through deposition.Ice-phase particles transform into graupel and snow particles through processes such as aggregation， Bergeron process， collection， and collision-coalescence.As they descend， ice-phase particles melt and combine with cloud water， accelerating the conversion of cloud water to rainwater.

Emission and backscattering at different frequencies have varied responses to soil physical processes (e.g., moisture redistribution, freeze-thaw) and vegetation growing/senescencing. Combing the use of active and passive microwave multi-frequency signals may provide complementary information, which can be used to better retrieve soil moisture, and vegetation biomass and water content for ecological applications. To this purpose, a Community Land Active Passive Microwave Radiative Transfer Modelling Platform (CLAP) was adopted in this study to simulate both emission (TB) and backscatter (σ0), in which the CLAP is backboned by the TorVergata model for modelling vegetation scattering, and an air-to-soil transition model (ATS) (accounting for surface dielectric roughness) integrated with the Advanced Integral Equation Model (AIEM) for modelling soil surface scattering. The accuracy of CLAP was assessed by both ground-based and spaceborne measurements, and the former was from the deployed microwave radiometer/scatterometer observatory at Maqu site on an alpine meadow over the Tibetan plateau. Specifically, for the passive case, simulated TB (emissivity multiplied by effective temperature) were compared to the ground-based ELBARA-III L-band observations, as well as C-band Advanced Microwave Scanning Radiometer 2 (AMSR2) and L-band Soil Moisture Active Passive (SMAP) observations. For the active case, simulated σ0 were compared to the ground-based scatterometer C- and L-bands observations, and C-band Sentinel and L-band Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) observations. This study is expected to contribute to improving the soil moisture retrieval accuracy for dedicated microwave sensor configurations.

Soil freeze–thaw (FT) cycles impact soil functions and atmosphere–land interaction, but accurate measurements are very limited. Since surface dielectric properties and microwave emissions are sensitive to the FT state, brightness temperature (TB) measurements at L-band allow retrieval of the FT state. We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive (SMAP) TB measurements. This retrieval algorithm is formulated regarding Diurnal Amplitude Variation (DAV), which is defined as the difference in TB observations of ascending and descending orbits. The DAV-FT algorithm uses globally fixed parameters. However, parameters should vary regionally considering factors like land cover type, terrain, and climate regions. We introduce Overall Classification Accuracy (OA) to characterize the extraction of DAV annual variation under different parameters. Then, the parameter optimization process, akin to maximum likelihood estimation, selects a combination of parameters to extract the annual variation of the DAV optimally. The DAV-FT algorithm uses optimized parameters, and the results show that compared to using fixed parameters, (a) the area with OA > 0.7 increases from 54.43% to 89.36%; (b) consistency with ERA5-Land and SMAP data has improved in southwestern North America, the Qinghai–Tibet Plateau, and southwestern Eurasia, with regions showing over 0.7 consistency reaching 81.28% for ERA5-Land and 79.54% for SMAP-FT; and (c) in situ stations with higher accuracy outnumber those with lower accuracy (48.11% versus 22.97% for fixed parameters, 35.14% versus 33.51% for SMAP FT). Furthermore, the algorithm achieves the highest median (0.92) and median accuracy (0.88), compared to fixed parameters and SMAP.

Accurately determining the freeze/thaw state (FT) is crucial for understanding land-atmosphere interactions, with significant implications for climate change, ecological systems, agriculture, and water resource management. This article introduces a novel approach to assess FT dynamics by comparing the new diurnal amplitude variations (DAV) algorithm with the traditional seasonal threshold algorithm (STA) based on the soil moisture active passive (SMAP) brightness temperature data. Utilizing soil temperature profiles from 44 sites recorded by the National Ecological Observatory Network between July 2019 and June 2022. The results reveal that the DAV algorithm demonstrates a remarkable potential for capturing FT signals, achieving an average accuracy of 0.82 (0.89 for the SMAP-FT product) across all sites and a median accuracy of 0.94 (0.92 for the SMAP-FT product) referring to soil temperature at 0.02 m. Notably, the DAV algorithm outperforms the SMAP-adopted STA in 25 out of 44 sites. The accuracy of the DAV algorithm is affected by daily temperature fluctuations and geographical latitudes, while the STA exhibits limitations in certain regions, particularly those with complex terrains or variable climatic patterns. This article's innovative contribution lies in systematically comparing the performance of the DAV and STA algorithms, providing valuable insights into their respective strengths and weaknesses.

Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations.

During cold wave movements, land-atmosphere interaction functions as a "switch" mechanism, regulating energy and moisture exchange. However, existing methods have limitations in capturing this process. The dielectric contrast between frozen and thawed soil induces diurnal amplitude variation (DAV) in brightness temperature at L-band, providing a basis for tracking land-atmosphere interactions. Our study shows that DAV effectively captures cold air masses' origin and seasonal movement. During a cold wave, its phase relationship with air and soil temperatures falls into four categories: precedes (-1.1 to -0.6 days), lags (0.71-0.98 days), in between (-0.62 to 0.34 days), and synchronized. Moreover, DAV lags air temperature in 63% of cases and precedes soil temperature in 59%, consistently fluctuating within their overlap region. This suggests DAV is a more direct indicator of freeze-thaw, reflecting the conditions at the atmosphere-land interface.

The Yarlung Tsangbo Grand Canyon (YGC) is located in the southeastern Tibetan Plateau and often experiences heavy precipitation, which can cause flooding and landslides. The scarcity of meteorological observations in the YGC limits our understanding of the mechanisms behind heavy precipitation in this region. In this study, we conducted 1-km numerical simulations of six heavy precipitation events to uncover their common mechanisms. The effectiveness of both cumulus parameterization and orographic drag schemes was evaluated. The results indicated that the multiscale Kain-Fritsch scheme (MSKF) outperformed the no cumulus scheme (NO_CU) in at least four out of six events. Further evaluations of four precipitation intensities during the six events also revealed that MSKF has reduced overestimation of light precipitation (Mean Absolute Error, MAE, was decreased by 15.4

This study utilizes a high-resolution emission inventory and the WRF-CMAQ modeling system to ana-lyze the temporal and spatial evolution of tropospheric NO₂ vertical column density(VCD)derived from TRO-POMI satellite data.It also provides a preliminary assessment of the uncertainty in the NOₓ emission inventory for the Sichuan Basin in 2019.The findings reveal elevated tropospheric NO₂ VCD in areas with intense anthro-pogenic activity,including the Chengdu Plain,southern Sichuan's urban clusters,and Chongqing,while the central Sichuan Basin remains relatively clean.Seasonal variations,influenced by both meteorological condi-tions and anthropogenic emissions,show significantly higher NO₂ VCD in winter and spring compared to sum-mer and autumn.A comparison between the WRF-CMAQ model and TROPOMI satellite data for January 2019 indicates strong agreement in cleaner regions,though TROPOMI reports notably higher NO₂ VCD in high-emis-sion cities such as Chengdu and Chongqing,suggesting that the emission inventory may underestimate NOₓ emis-sions in megacities.This work underscores the need for stringent NOₓ emission controls in major cities,such as Chengdu and Chongqing,while also emphasizing the urgency of enhancing emission controls in medium-sized cities across the Sichuan Basin.

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m3/m3. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography.