Thank you to the authors for this study, which shows that E-OBS offers high-quality meteorological data that can be used for large-sample hydrology studies in Europe, while also highlighting the limitations of E-OBS clearly. The manuscript is easy to follow and of overall high quality. There are some minor comments that should be addressed, the overall quality of the manuscript is already good.

Minor specific comments:

• L11: “limitations of data quality” -> maybe indicate that data quality is expected to vary in space? (e.g. “limitations and regional variations of data quality”)
• L36-L39: The number of catchments is not directly the problem, the mixture of different  regions / countries is the challenge, as meteorological data is often available on a national level (e.g. provided by national meteorological organizations)
• L40, L45, L55, L72, L417…: I know what you want to say here, but I don’t like the word “standardization” in this context, as it usually refers to something else when it comes to data processing, and e.g. ERA5 or E-OBS are just datasets on a larger scale with different sources and processing methods, they do not “standardize” smaller datasets.
• L91-L93: Why did you apply these criteria?
• L111-L112: The resolution of 0.25° of E-OBS is very coarse, I know that e.g. the precipitation data for CAMELS-DE has a resolution of 1x1 km, this could be an additional source for limitations of Estreams data, also for comparisons in this study. Maybe you could think about an update of Estreams in the future? I think this could be worth it (not part of this study).
• L116-L117: I think the main thing here is that the quality and uncertainty of E-OBS data have a larger (regional) spread, some regions will have very good quality data (where station measurements are available), other regions with less station measurements will have worse data quality. Even if the data comes from the same source (E-OBS), quality and uncertainty varies regionally. I think this is a major challenge in LSH and people need to be aware of this.
• L149: Maybe add a small explanation on why you designed the scenarios this way, and which questions you aim to answer with the different scenarios (I and II are quite clear, but why did you do III-V?)
• L152: This could also go into limitations, but the catchment shapes are also not identical between EStreams and the CAMELS datasets, which results in different areas for which the meteorological data was “cut out” and aggregated, which can also lead to differences.
• L312: I think it is hard to see any patterns in this figure with the mixture of scenario I and II with circles and triangles. I am not sure on how to improve the figure, but you could calculate a regression line and also report the p-values? This could also be used to back up your statement in L310-311
• L324-L325: results in Austria are bad as ERA5 data is used, not a “local” dataset, maybe add this here?
• L344-L353: Here you have the paragraph about limitations of ERA5 data in Austria, but I think it does not really fit in the paragraph (“Evaluation of the E-OBS data in comparison to the E-OBS station density”). Maybe it could fit better in Section 4.1? I think the point about ERA5 data used in Austria is very important in this study, as this is a fundamental difference to the other CAMELS datasets, where local, highest-quality data is used, in Austria it is quite the opposite. You should make this point very clear, also in the beginning, as you do not test whether "local" CAMELS data is better than E-OBS data in the case of Austria.
  
  It would also be interesting to see how the different CAMELS precipitation data was collected / processed (maybe not so easy to find out). I only know about CAMELS-DE, but HYRAS is also based on interpolated station data (I guess mostly the same stations as used for E-OBS), which would explain the relative similarities, but it is still interesting to see that there are differences (maybe due to different interpolation / processing methods or the coarser resolution of E-OBS)
• L398-L401: For smaller catchments, having E-OBS data from the 0.1° version could also help (again, maybe this is worth an update for EStreams, which of course is not part of this study, just a general suggestion)
• L402…: You could add to the conclusion that local datasets are usually the best, but using E-OBS data and EStreams offer a great harmonized data source for LSH studies covering all of Europe, especially as an alternative to ERA5 which has shown limitations in Austria. Maybe extend a little bit on this and how E-OBS could be an alternative to ERA5 which was mostly the standard before.
  
  So in general, we now have a great (still growing) coverage of streamflow data in Europe through different national CAMELS datasets now. With the addition of, especially compared to ERA5, high quality E-OBS forcing data, readily available in EStreams, there is a great data basis for LSH studies covering the entirety of Europe.

Technical Corrections

• L27: “too” -> “as well”
• L42: “than” -> “compared to”
• L203: “(Fig. 2Figure 2)”
• 277: “was the performance gap between scenarios II and I…” -> “the performance gap between scenarios II and I was…”

See attached file

This paper compares the performance of two forcing datasets used for calibration with a classical hydrological model. Overall, I liked the paper, the idea to compare the quality of forcings using a model, without needing to assume that the model itself is an unflawed representation of the hydrological reality.

Major remarks

• First, your paper would deserve a better title. As I understood, you ask a much more general and (from my point of view) interesting question: how can we use a classical precipitation-runoff model such as HBV in order to compare the quality of precipitation data. I believe you should put this point at the forefront of your paper. You should discuss the “good sense” (almost philosophical) hypothesis of your approach: even if your hydrological model is imperfect, the difference of efficiency when calibrating the model with different forcings cannot be due to some random factor. Better performances cannot be due to chance. You could perhaps look at this chapter of the famous Ray Linsley (1982) who discussed the topic, I only remember this short citation “if the data are too poor for the use of a good simulation model they are also inadequate for any other model”, but there must be some other interesting citations there.

• Second, I believe that it is worth comparing the CAMELS outputs with the E-obs outputs, introducing a further class of inputs (ERA-5) makes things more complex. I would simply have discarded the LAMAH dataset, stating that you aim at comparing the “best ground-based estimate” of the CAMELS datasets with the E-obs... it is definitely not a big surprise that ERA-5 estimates are not good... and it makes your paper unnecessarily more complex. You do not have to show us everything you have done, if you have pushed open at a few open doors in the course of your research (what we all do...) you do not need to tell us about it.
• Third, I was wondering whether it would have been interesting to restrict the dataset to the less-regulated (reservoir-impacted) catchments. I know for example that there are quite a few regulated catchments in the Swiss CAMELS dataset. It will not change the results, but a focus on the less regulated catchments could perhaps show even clearer differences.

Minor remarks

• I believe that before mentioning (l.36) that “the inclusion of an increasing number of catchments in one dataset almost always goes hand in hand with difficulties in providing high-quality forcing data” you should underline that large samples also come with their load of problematic discharge stations. In my experience of building a CAMELS dataset, a large part  of the effort was absorbed by scrutinizing collectively the time series, the locations, etc. And because E-streams did not make any sorting, there must be along with the hydrometric stations a few (or more) non sense stations (probably a few buoys in France...) or at least stations which measure a level that cannot be related to any significant hydrological flux.
• Did you check that the discharge data were exactly the same in E-stream and CAMELS?
• In 3.3.1 (Number of E-OBS precipitation stations): I believe you should mention that the number of E-Obs stations is correlated with the size of the catchments... and as you (and all the conceptual modelers) know, the largest catchments get the best KGE criteria.

References

Linsley, R.K., 1982. Rainfall-runoff models-an overview. In: V.P. Singh (Editor), Proceedings of the international symposium on rainfall-runoff modelling. Water Resources Publications, Littleton, CO, pp. 3-22.