This study presents gap-free monthly gravity field solutions up to degree and order 60 for January 1993-December 2024 using constrained least-squares collocation (LSC), which jointly performs solution combination and denoising without explicit filtering. The proposed product outperforms existing reconstructions based on hydrometeorological forcing and static spatial modes, yielding smaller sea-level budget misclosures, reduced basin-scale water-balance errors, and stronger agreement with IMBIE (Ice Sheet Mass Balance Inter-comparison Exercise) estimates. Overall, the work is meaningful, the methodology is robust, and the results are convincing. I therefore recommend acceptance after some revisions.

• Line 75: The time span “1984 to 2022” should be revised to “1984 to 2023.”
• Table S2 in the supplementary file presents complex pseudocode and is not visually clear; could the authors replace it with a flowchart to improve readability and better illustrate the workflow?
• The labels/captions of Fig. 3(d) and Fig. 3(e) should be checked to confirm whether they are reversed.
• Notation and abbreviations should be standardized throughout the manuscript (e.g., TVGF/TVGFS, d/o, SHC/SHCs, NSS/NSSs, capitalization of defined terms) to avoid ambiguity.
• A short “Notation” box (or one supplementary table) listing core symbols (, , , , , , etc.) and their dimensions would improve readability..
• Section titles could be revised for parallel style and precision (e.g., “Function Model” → “Functional Model”; “Result Evaluations” → “Results and Evaluation”).
• Please report the analysis period consistently in all figure captions (e.g., 1994-2020, 2002-2024), as some captions currently list only metrics without the corresponding time span.
• A quick language polish is recommended to fix recurrent minor issues, including subject-verb agreement (e.g., results indicates → results indicate) and number/possessive form (e.g., basin’s → basin-scale or basins’). These edits do not affect the methodology but will noticeably improve the manuscript’s professional quality.
• One concern is why IGG-SLR-HYBRID was adopted in the combination, whereas IGG-SLR-DORIS was used in the comparison. Since these two models seem to be based on similar methodologies and exhibit comparable performance over 1994–2020, the authors may wish to clarify whether it would be sufficient to include only one of them.
• For greater user convenience, the authors are encouraged to reorganize the combined spherical harmonic solutions into the standard monthly .gfc format adopted by the ICGEM website. Moreover, the current 1° gridded data do not include the corresponding uncertainty information or the associated latitude and longitude coordinates. It is therefore recommended that these gridded products be provided in NetCDF format. It would also be highly beneficial if the authors could provide 0.5° gridded data together with their associated uncertainty estimates, thereby offering users a wider range of choices.

The authors generate a long-term TWSA dataset by combining different gravity satellite missions. This work seems unique in comparison to current widely available long-term TWSA dataset, which is generally generated using climatic inputs. It should be valuable for understanding climate change impacts on global terrestrial water storage change from independent satellite observations. I have some minor comments before it can be accepted.

• It is interesting to see how it differs from that based on climatic inputs results, for example, Mandal et al., 2025, and/or Li et al., 2021.
• Line 24: why the reductions by LSC-CS are not significant, and is this small improvement helpful.
• The word evaporation is better to be changed to evapotranspiration.
• Since the simulated runoff may differ from the truth. The in situ gauged streamflow data (for example, from GRDC) for some selected river basins can be used for water balance closure, and hopefully it may improve the validation.
• A spatial distribution map showing the long-term TWSA trend can be provided for a better understanding of the result, and maybe its differences with other results.

References:

(1) Li et al., 2021, GRL, Long-Term (1979-Present) Total Water Storage Anomalies Over the Global Land Derived by Reconstructing GRACE Data.

(2) Mandal et al., 2025, ESSD, Machine-learning-based reconstruction of long-term global terrestrial water storage anomalies from observed, satellite and land-surface model data