Terrestrial water storage (TWS) is a pivotal component of the global water cycle, profoundly impacting water resource management, hazard monitoring, and agriculture production. The Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-On (GFO), have furnished comprehensive monthly TWS data since April 2002. However, there are 35 months of missing data over the entire GRACE/GFO observational period. To address this gap, we developed an operational approach utilizing singular spectrum analysis and principal component analysis (SSA-PCA) to fill these missing data over mainland China. The algorithm was demonstrated with good performance in the Southwestern River Basin (SWB, correlation coefficient, CC: 0.71, RMSE: 6.27 cm), Yangtze River Basin (YTB, CC: 0.67, RMSE: 3.52 cm), and Songhua River Basin (SRB, CC: 0.66, RMSE: 7.63 cm). Leveraging two decades of continuous time-variable gravity data, we investigated the spatiotemporal variations in TWS across ten major Chinese basins. According to the results of GRACE/GFO, mainland China experienced an average annual TWS decline of 0.32 ± 0.06 cm, with the groundwater storage (GWS) decreasing by 0.54 ± 0.10 cm/yr. The most significant GWS depletion occurred in the Haihe River Basin (HRB) at −2.07 ± 0.10 cm/yr, significantly substantial (~1 cm/yr) depletions occurred in the Yellow River Basin (YRB), SRB, Huaihe River Basin (HHB), Liao-Luan River Basin (LRB), and Southwest River Basin (SWB), and moderate losses were recorded in the Northwest Basin (NWB, −0.34 ± 0.03 cm/yr) and Southeast River Basin (SEB, −0.24 ± 0.10 cm/yr). Furthermore, we identified that interannual TWS variations in ten basins of China were primarily driven by soil moisture water storage (SMS) anomalies, exhibiting consistently and relatively high correlations (CC > 0.60) and low root-mean-square errors (RMSE < 5 cm). Lastly, through the integration of GRACE/GFO and Global Land Data Assimilation System (GLDAS) data, we unraveled the contrasting water storage patterns between northern and southern China. Southern China experienced drought conditions, while northern China faced flooding during the 2020–2023 La Niña event, with the inverse pattern observed during the 2014–2016 El Niño event. This study fills in the missing data and quantifies water storage variations within mainland China, contributing to a deeper insight into climate change and its consequences on water resource management.

A global gravity field model (GGM) is essential to be validated with ground-based or airborne observational data for the accurate application of the GGM at a regional scale. Furthermore, accurately understanding the commission errors between the GGM and observational data are crucial for improving regional gravity fields. Taking the North China region as an example, to circumvent the omission errors, it is necessary to unify the spatial resolutions of the EIGEN-6C4 model and terrestrial gravity observational data to 110 km (determined by the distribution of gravity stations) by employing the spherical harmonic function for the EIGEN-6C4 model and the Slepian basis function for the gravity data, respectively. However, the application of spherical harmonic function expansions in the gravity model results in the Gibbs phenomenon, which may be a primary factor contributing to commission errors and impedes the accurate validation of the EIGEN-6C4 model with terrestrial gravity data. To effectively mitigate this issue, this study proposes a combination approach of window function filtering and regional eigenvalue constraint (based on the Slepian basis). Utilizing the EIGEN-6C4 gravity model to derive the gravity disturbance field at a resolution of 110 km (with spherical harmonic expansion up to the 180th degree and order), the combination approach effectively suppresses over 90% of high-degree (above the 120th degree) Gibbs phenomena. This approach also reduces signal leakage outside the region, thus enhancing the spatial accuracy of the regional gravity disturbance field. A subsequent comparison of the regional gravity disturbance field derived from the true model and terrestrial gravity data in North China indicates excellent consistency, with a root mean squared error (RMSE) of 0.80 mGal. This validation confirms that the combined approach of window function filtering and regional eigenvalue constraints effectively mitigates the Gibbs phenomenon and yields precise regional gravity fields. This approach is anticipated to significantly benefit scientific applications such as improving the accuracy of regional elevation benchmarks and accurately inverting the Earth’s internal structure.

The Pearl River Basin (PRB) is a humid subtropical system where frequent floods and recurrent droughts challenge water management. GRACE and GRACE Follow-On provide basin-scale constraints on terrestrial water storage anomalies (TWSA), yet their coarse native resolution limits applications at regional scales. We employ a downscaled TWSA product derived via a joint inversion that integrates GRACE/GFO observations with the high-resolution spatial patterns of WaterGap Global Hydrological Model (WGHM). Validation against GRACE/GFO shows that the downscaled product outperforms WGHM at basin and pixel scales, with consistently lower errors and higher skill, and with improved terrestrial water flux (TWF) estimates that agree more closely with water balance calculations in both magnitude and phase. The TWSA in the PRB exhibits strong seasonality, with precipitation (P) exceeding evapotranspiration (E) and runoff (R) from April to July and storage peaking in July. From 2002 to 2022, the basin alternates between multi-year declines and recoveries. On the annual scale, TWSA covaries with precipitation and runoff, and large-scale climate modes modulate these relationships, with El Niño and a warm Pacific Decadal Oscillation (PDO) favoring wetter conditions and La Niña and a cold PDO favoring drier conditions. extreme gradient boosting (XGBoost) with shapley additive explanations (SHAP) attribution identifies P as the primary driver of storage variability, followed by R and E, while vegetation and radiation variables play secondary roles. Drought and flood diagnostics based on drought severity index (DSI) and a standardized flood potential index (FPI) capture the severe 2021 drought and major wet-season floods. The results demonstrate that joint inversion downscaling enhances the spatiotemporal fidelity of satellite-informed storage estimates and provides actionable information for risk assessment and water resources management.

Publicly available global high-resolution terrestrial water storage anomaly (TWSA) datasets derived from satellite gravimetry remain scarce. Many existing global downscaling products rely heavily on hydrological models. Consequently, their performance can degrade in regions where key mass variations observed by the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GFO) are poorly represented in the models, notably those associated with mountain glaciers and large lakes. Here we provide SYSU TWSA, a global monthly 0.5° TWSA dataset spanning April 2002 to December 2022, generated using a joint-inversion spatial downscaling framework that integrates large-scale constraints from GRACE/GFO, high-resolution spatial patterns from the WaterGAP Global Hydrological Model (WGHM), and additional mascon groups that explicitly represent mountain glaciers and selected large or rapidly changing lakes. The dataset helps alleviate the current shortage of global high-resolution products and explicitly strengthens the representation of glacier- and lake-related signals. We assess SYSU TWSA through four complementary evaluations: (1) basin-wise consistency with raw GRACE/GFO estimates, (2) a basin water-balance consistency check, (3) independent evaluation against in situ groundwater well observations, and (4) comparisons with representative downscaled products in both the spectral and spatial domains. SYSU TWSA shows strong agreement with GRACE/GFO at the basin scale, with coefficients of determination (R²) exceeding 0.85 across basin-size classes. In small basins, consistency with terrestrial water fluxes derived from the basin water-balance equation improves substantially, with NSE increasing by 17.1 % relative to raw GRACE/GFO across 1,200 basins. Agreement with groundwater wells also improves, with correlations increasing at 67.7 % of 28,248 wells. Comparisons with representative assimilation-based and deep-learning downscaled products further indicate that SYSU TWSA achieves competitive overall accuracy while strengthening the representation of glacier- and lake-related signals.

Publicly available global high-resolution terrestrial water storage anomaly (TWSA) datasets derived from satellite gravimetry remain scarce. Many existing global downscaling products rely heavily on hydrological models. Consequently, their performance can degrade in regions where key mass variations observed by the Gravity Recovery and Climate Experiment (GRACE) and its successor mission GRACE Follow-On (GFO) are poorly represented in the models, notably those associated with mountain glaciers and large lakes. Here we provide SYSU TWSA, a global monthly 0.5° TWSA dataset spanning April 2002 to December 2022, generated using a joint-inversion spatial downscaling framework that integrates large-scale constraints from GRACE/GFO, high-resolution spatial patterns from the WaterGAP Global Hydrological Model (WGHM), and additional mascon groups that explicitly represent mountain glaciers and selected large or rapidly changing lakes. The dataset helps alleviate the current shortage of global high-resolution products and explicitly strengthens the representation of glacier- and lake-related signals. We assess SYSU TWSA through four complementary evaluations: (1) basin-wise consistency with raw GRACE/GFO estimates, (2) a basin water-balance consistency check, (3) an independent evaluation against in situ groundwater well observations, and (4) comparisons with representative downscaled products in both the spectral and spatial domains. SYSU TWSA shows generally good agreement with GRACE/GFO at the basin scale, with coefficients of determination (R2) exceeding 0.85 across basin-size classes. In small basins, consistency with terrestrial water fluxes derived from the basin water-balance equation improves substantially, with NSE increasing by 17.1 % relative to raw GRACE/GFO across 1200 basins. Agreement with groundwater wells also improves, with correlations increasing at 67.7 % of 28 248 wells. Comparisons with representative assimilation-based and deep-learning downscaled products further indicate that SYSU TWSA demonstrates competitive overall accuracy while strengthening the representation of glacier- and lake-related signals. The SYSU TWSA dataset is openly available at the National Tibetan Plateau Data Center (https://doi.org/10.11888/Terre.tpdc.303322, Xiong et al., 2026).

Noise and the necessary smoothing introduced by post-processing filters fundamentally degrade the spatial resolution of temporal gravity field products from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, thus limiting their utility for investigating terrestrial water storage variations. To address this issue, this study presents a generalized reconstructed filtering framework that enhances conventional filters, including Gaussian, Fan, and DDK kernels. The framework converts iterative filtering operations into analytically expressed equivalent filters, explicitly characterizing their impact on signal and noise. Evaluations using both real-world data and controlled closed-loop simulations demonstrate that the reconstructed filters achieve higher empirical signal-to-noise ratios compared to their original counterparts. Among them, the DDK-based reconstructed filter performs best, yielding the highest signal-to-noise ratio. We further demonstrate that the land-ocean root-mean-square ratio can serve as a reliable empirical proxy for the signal-to-noise ratio in global or regional real-world applications. The proposed framework provides an adaptable and effective means of balancing noise suppression with spatial resolution in the investigation of terrestrial water storage variations.