I enjoyed reading this fruitful manuscript. I only have several concerns and comments listed below.

-L186: the readers would except a similar treatment of the models for consistency of the framework. Except for the objective function (KGE), all other experimental details are different. Different search algorithms (SCE vs DDS), different max iterations (may be understandable). The chaotic structure of the paper makes is difficult to enjoy the fruitful outcomes. Currently it reads like a technical report by the corps engineers doing everything for better KGE. However, the research design needs a framework. The experiments should focus on effects of one thing one at a time. Using two different models is important for structural uncertainty; however under the same P, PET, Tavg  input and methodology (calibration algorithm, max iterations etc).

-Please a conceptual diagram/framework describing your methodology.

-Metric: Both models are top quality spatially semi or fully distributed models. Using a metric focusing on average flows may not fit best while NSE focuses more on high flows. There are other metrics focusing on patterns of simulated flux maps such as SSIM, FSS, EOF, SPAEF and SPEM. For novelty, it is recommended to elaborate calibration approach for the two models.

-Did authors applied a sensitivity analysis before the calibration to select most important parameters for the streamflow using selected metric, KGE?

-Figure4: For process consistency, different remote sensing products could be used to evaluate model results. Why did you only focus on streamflows, if the models are capable of producing AET, SM (at different soil horizons). MODIS, SMAP, SMOS, ESA, ALEXI all are useful constraints for model calibration making the novelty maximized. Literature review of the paper misses all those calibration papers focusing not only KGE but also RS products.

https://doi.org/10.1002/2017WR021346

https://doi.org/10.5194/hess-22-1299-2018

-Do these models incorporate pedo-transfer functions for parameter regionalization? Some of the distributed models, like mHM, uses multi-parameter regionalization (MPR) technology.

https://doi.org/10.5194/gmd-15-859-2022

-Table 1: “VIC4.1.2”

Why this old version of VIC model is used while current version WRF-Hydro 5.2.0 is preferred.

VIC5 version includes many infrastructure improvements (glaciers etc) as described here:

https://doi.org/10.5194/gmd-11-3481-2018

-Figures 9-10-11 can be given in appendix.

-The paper needs a separate Discussion section and a separate Conclusions (bullets) section. Summary can be appropriate for “engineering corps” reports not for HESS papers.

General comments

This study presents the calibration and regionalisation of two land-surface hydrologic models in 263 catchments in the Western US. The results indicate that the median Kling-Gupta efficiency obtained in model calibration and regionalisation outperforms earlier/baseline study ().

The study focuses on an interesting topic, but in its current form, it reads more like a technical report than a research paper. The Introduction nicely presents the context (i.e. why it is important to simulate water cycle components in the study region accurately). Still, the synthesis and formulation of the current research gaps need to be significantly improved. There is a large body of literature focusing on regional calibration of hydrologic models, transfer of model parameters at the regional scale, definition of the signatures used for similarity definition etc. It needs to be made clear how this study goes beyond the existing studies. The formulation of the research questions needs to be very precise and linked with presenting the research gaps. In its current form, it needs to be clarified whether the main goal is to propose and evaluate some methodological advance in model calibration and/or regionalisation or to present some new factual information about the study region (a case study analysis).

The selection of the two models needs to be better justified. What are the differences in runoff generation between the models (and how is it linked with the regional variability of runoff generation in the study region)? It needs to be clarified why to use a 3hr simulation time step, when model inputs are daily. It is also not clear why to calibrate only selected soil-related parameters and how the selection is linked with the runoff generation processes and their variability in the study region. For example, are the snow accumulation and melt processes less important? Or are the snow-related model parameters already accurately calibrated? More importantly, the results and the differences between the two models need to be better linked with the main runoff generation processes (and their regional variability).

I missed the discussion of the results, which will link the new findings with previous studies. This can enhance the demonstration of the novel scientific contribution of the study.