This manuscript addresses practical challenges in deploying the VIC model, particularly the tedious preparation of input data and model parameters. The authors present Easy VIC Build (EVB), an open-source Python package that integrates these procedures into a unified workflow, and demonstrate its applicability in two real-world basins with different modeling settings. The integration of MPR further enhances the potential of EVB to support accessible spatially distributed parameterization. Overall, the manuscript is of high quality, fits well within the scope of the journal, and should be of interest to an international readership. In addition, the manuscript examines the relationship between the spatial distribution of model parameters and runoff generation from the perspective of physical plausibility, providing an indirect assessment of the rationality of the MPR approach. Although such an analysis is not commonly adopted, I find this aspect particularly interesting and believe that it deserves further discussion. I therefore minor revision, with several points regarding the presentation and wording to be addressed, as outlined below.

1. As noted above, the authors examine the physical plausibility of the spatial patterns of model parameters, which is an important advantage facilitated by the application of the MPR technique. However, the manuscript does not appear to provide sufficient discussion of the estimation of spatially distributed parameters, and thus this advantage is not fully highlighted. I therefore suggest that the authors expand the discussion of spatial parameter estimation, including (1) what alternative approaches, besides MPR, can be used to estimate spatially distributed parameters, (2) what are the major challenges and difficulties associated with spatial parameter estimation, and (3) what are the specific advantages of MPR in addressing these challenges. Such a discussion would help better contextualize the contribution of MPR and further highlight the significance of the spatial parameter patterns obtained in this study.
2. A key aspect that is currently missing from the manuscript is a clear description of the transfer functions, particularly which predictor variables are used to estimate each model parameter. I suggest that the authors provide a summary table in Section 3.1 specifying the predictor-parameter relationships. The corresponding transfer functions could also be presented explicitly, similar to those given in Eqs. (2)–(4). Providing this information would make the parameter estimation procedure more transparent and help readers better understand the implementation of the proposed approach.
3. Given the length of the manuscript, I suggest that the authors consider reducing or streamlining some of the less essential content to improve the overall readability and focus of the manuscript. The space saved through these revisions could instead be used to further strengthen several aspects that are more closely related to the scientific contribution of the study.  Specifically:
   • Section 2.1.3: This subsection could be removed from the main text or reduced to a brief description and incorporated elsewhere, as the current level of detail does not appear essential to understanding the main contribution of the study.
   • Section 3.3: I suggest merging and substantially condensing the subsections under this section to provide a more concise description of the workflow and improve readability.
   • Section 4.1: This section could also be shortened, as the detailed description of the case-basin setup does not appear to be central to the main findings of the manuscript.
   • Section 5.3.2: I suggest expanding the discussion of spatially distributed parameter estimation as mentioned above, particularly the challenges associated with this process and the advantages of the MPR approach.
   • Abstract and Conclusion: I recommend further strengthening these sections by incorporating more quantitative results, such as specific model performance metrics and the accuracy achieved through cross-scale parameter transfer. Including these quantitative findings would allow readers to more readily assess the effectiveness and broader applicability of the proposed approach.
4. The manuscript also contains several minor formatting and writing issues that should be addressed. For example:
   • Line 417: A line break appears to be missing. Please check and correct the formatting accordingly. 
   • Appendix C: The title appears to be identical to that of Appendix B. Please verify the correct title and revise it accordingly.

The EVB framework addresses a real and important bottleneck in VIC-5 deployment. Its Python-based, object-oriented design with integrated MPR is well conceived, and the two case studies cover both large-sample and site-specific application scenarios. The main weaknesses lie in lack of comparison with existing tools and insufficient methodological transparency.

Detailed Comments

1. There is no quantitative comparison with existing tools. The introduction criticizes VIC-ASSIST at some length, yet no functional or performance comparison is provided. A tool paper should address: How much faster is EVB than VIC-ASSIST? How much more flexible? What can EVB do that VIC-ASSIST cannot? A feature comparison matrix (model versions supported, data formats, calibration algorithms, parameter regionalization, parallel computing, etc.) is recommended. The positioning of EVB relative to alternatives such as the VIC Python driver ecosystem and SUMMA+mizuRoute also deserves brief discussion.
2. MPR transfer functions are not specified; methodological transparency is insufficient. MPR is presented as a core EVB feature, yet the mathematical form of the transfer functions is entirely absent — how soil properties map to Ks via g-parameters, how topography influences binfilt, whether vegetation parameters go through MPR or direct resampling, etc. Open-source code does not substitute for methodological transparency in the paper. Additionally, the selection of the 15-parameter reduced calibration set rests on two literature citations without an independent sensitivity analysis in this study.
3. Two-year warm-up periods may be short for a distributed model with deep soil storage and baseflow reservoirs. More discussion of this choice would be helpful.
4. The synthetic-rainfall parameter validation is somewhat circular: under homogeneous forcing and initial conditions, spatial variability is purely parameter-driven.
5. Computational efficiency is reported for only one JRB configuration; multi-resolution comparisons would be informative.
6. A brief discussion of EVB vs. SUMMA+mizuRoute positioning would help readers understand use cases.