dear authors,

first of all, let me apologize for the time that this review has taken. This, as you may have supposed, has had a lot to do with two quite contrasting reviews that arrived from the selected referees.
The indecisiveness of the reports forced us to ask for a third review, which unfortunately arrived after the review acceptance time. So, I was left with the only solution of revising the paper myself.
After all this, and despite some issues that you will find below, I am glad to inform you that the paper can be accepted should you be willing to apply some minor modifications.
Please note that, here, "minor" should be intended as something below "major" but still important, in my opinion. So, no other review round will be needed but I will revise your modifications personally, to expedite the acceptance process.
Now, going to the point, the main issues of the manuscript are those highlighted in the review reports, which you will find attached, plus the following, partially overlapping, points:

1. classical susceptibility does not usually include time - it is intended to produce a statistical inference on the relative probability in space. As a consequence, susceptibility is usually based on historical inventories and tries to predict the occurrence of landslides over a time span of the same order of magnitude as the time span of the inventory itself (uncertainties and missing information considered). Instead, you propose here event-based landslide maps to train a statistical model, which seems to be valid only for similar triggering events. In this way, to the extent that your climate-change forecasts are correct, you are predicting the relative response of the landscape to this event type, only. This poses two main concerns:
1A. How can then your samples (being based only on a few rare, documented events) be statistically representative of all the future expected extreme rainfall events?
1B. How can you support (statistically) the inference that the effect produced by the triggering event X in position Y is able to produce the same effect in position Z (where local conditions are different from site Y)?
I am not saying that you model is flawed, as I do not have sufficient information to say so, or the opposite. But maybe you could try to better frame the limits and conceptual context in which you are working. The approach may well be new, but at the moment we can’t say if it is truly appropriate for the usual purposes of susceptibility mapping, that is scenario building, land planning, or risk reduction.

2. The topic in general is far from new. There are several papers published in the last 10 years that propose multi-temporal susceptibility across different climate change scenarios. It would be good if you listed at least some of them, to better set the background as well as to be able to better explain why your approach is novel. There are several papers, as well, that propose model sensitivity in statistical analysis of landslide hazard, including the influence of the different mapping parameters, scales, resolutions, sampling methods and so on. Please also enrich the list of references as related to this aspect. I do not want to add specific suggestions, as it is quite easy to find them. This would also help in framing your own research and the gaps you meant to fill.
3. As a side note, more extreme weather events of the same type are available in the study region, as far as I know. Did you try to include them in the analysis? Or did you try to use them as external validation of your model? (see also references cited below on the Austrian Alps).

4. Please consider that in the Eastern Alps, recent extreme events such as the Vaia storm of 2018 have highlighted that rainfall may start triggering landslides not just during the main storm, which only creates some preconditions, so to speak. But, instead, after several weeks. You use 5-day and 3-hour accumulated rain in your study. How can this influence your results? How can your statistics account for this delayed-time triggering?

Some minor points:
5. In lines 14-15 you report UNISDR and CRED data: Economic Losses, Poverty & Disasters (1998 - 2017) that cover a variety of disasters around the globe; I would suggest including one more study to the introduction, which was solely based on landslides, such as e.g.:
i) Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.

6. In lines 19-20, you might want to consider and include the recent works related to landslide susceptibility in the Austrian alps, such as:
i) Lima, P., Steger, S., & Glade, T. (2021). Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria. Landslides, 18(11), 3531-3546.
ii) Moharrami, M., Naboureh, A., Gudiyangada Nachappa, T., Ghorbanzadeh, O., Guan, X., & Blaschke, T. (2020). National-scale landslide susceptibility mapping in aaustria using fuzzy best-worst multi-criteria decision-making. ISPRS International Journal of Geo-Information, 9(6), 393.
iii) Gudiyangada Nachappa, T., Kienberger, S., Meena, S. R., Hölbling, D., & Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1), 572-600

7. In lines 63-64, you could try to address the bias associated with downscaling the climate data by using an approach as the one here:
i) Roberts, DR, Wood, WH, Marshall, SJ. Assessments of downscaled climate data with a high-resolution weather station network reveal consistent but predictable bias. Int J Climatol. 2019; 39: 3091– 3103. https://doi.org/10.1002/joc.6005

8. You provide no maps of susceptibility, which could help in identifying the locations of impending occurrences in a spatial way. Could you please add some of the maps (supplementary material as well)?

My recommendation is that you try to answer all reviewers’ questions on a point-to-point basis in a separate document, and modify the original manuscript accordingly, as well. Please also try to reply to the above questions and suggestions.
After you submit the modified version and the response letter, the revision process will continue directly to the Editor’s consideration, without further review rounds.
Thank you for submitting to NHESS and, again, my apologies
FC

dear authors,

first of all, let me apologize for the time that this review has taken. This, as you may have supposed, has had a lot to do with two quite contrasting reviews that arrived from the selected referees.
The indecisiveness of the reports forced us to ask for a third review, which unfortunately never arrived. So, we had to carefully consider the two reports in the light of the submitted document and materials.
On the basis of the referees’ reports, and the main points summarized below, the paper could be accepted for publication should you be willing to apply some major modifications.
The topic of the manuscript is of large interest to the NH community, and the approach used is quite novel and potentially providing insights into something that is still debated and unsolved. However, there are a few important issues present in the manuscript as highlighted in the review reports, which you will find attached, plus the following, partially overlapping, points:

1. classical susceptibility does not usually include time - it is intended to produce a statistical inference on the relative probability in space. As a consequence, susceptibility is usually based on historical inventories and tries to predict the occurrence of landslides over a time span of the same order of magnitude as the time span of the inventory itself (uncertainties and missing information considered). Instead, you propose here event-based landslide maps to train a statistical model, which seems to be valid only for similar triggering events. In this way, to the extent that your climate-change forecasts are correct, you are predicting the relative response of the landscape to this event type, only. This poses two main concerns:
1A. How can then your samples (being based only on a few rare, documented events) be statistically representative of all the future expected extreme rainfall events?
1B. How can you support (statistically) the inference that the effect produced by the triggering event X in position Y is able to produce the same effect in position Z (where local conditions are different from site Y)?
I am not saying that you model is flawed, as I do not have sufficient information to say so, or the opposite. But maybe you could try to better frame the limits and conceptual context in which you are working. The approach may well be new, but at the moment we can’t say if it is truly appropriate for the usual purposes of susceptibility mapping, that is scenario building, land planning, or risk reduction.

2. The main topic, despite being of general interest, is far from new. There are several papers published in the last 10 years that propose multi-temporal susceptibility across different climate change scenarios. It would be good if you listed at least some of them, to better set the background as well as to be able to better explain why your approach is novel. There are several papers, as well, that propose model sensitivity in statistical analysis of landslide hazard, including the influence of the different mapping parameters, scales, resolutions, sampling methods and so on. Please also enrich the list of references as related to this aspect. I do not want to add specific suggestions, as it is quite easy to find them. This would also help in framing your own research and the gaps you meant to fill.
3. As a side note, more extreme weather events of the same type are available in the study region, as far as I know. Did you try to include them in the analysis? Or did you try to use them as external validation of your model? (see also references cited below on the Austrian Alps).

4. Please consider that in the Eastern Alps, recent extreme events such as the Vaia storm of 2018 have highlighted that rainfall may start triggering landslides not just during the main storm, which only creates some preconditions, so to speak. But, instead, after several weeks. You use 5-day and 3-hour accumulated rain in your study. How can this influence your results? How can your statistics account for this delayed-time triggering?

Some minor points:
5. In lines 14-15 you report UNISDR and CRED data: Economic Losses, Poverty & Disasters (1998 - 2017) that cover a variety of disasters around the globe; I would suggest including one more study to the introduction, which was solely based on landslides, such as e.g.:
i) Froude, M. J. and Petley, D. N.: Global fatal landslide occurrence from 2004 to 2016, Nat. Hazards Earth Syst. Sci., 18, 2161–2181, https://doi.org/10.5194/nhess-18-2161-2018, 2018.

6. In lines 19-20, you might want to consider and include the recent works related to landslide susceptibility in the Austrian alps, such as:
i) Lima, P., Steger, S., & Glade, T. (2021). Counteracting flawed landslide data in statistically based landslide susceptibility modelling for very large areas: a national-scale assessment for Austria. Landslides, 18(11), 3531-3546.
ii) Moharrami, M., Naboureh, A., Gudiyangada Nachappa, T., Ghorbanzadeh, O., Guan, X., & Blaschke, T. (2020). National-scale landslide susceptibility mapping in aaustria using fuzzy best-worst multi-criteria decision-making. ISPRS International Journal of Geo-Information, 9(6), 393.
iii) Gudiyangada Nachappa, T., Kienberger, S., Meena, S. R., Hölbling, D., & Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1), 572-600

7. In lines 63-64, you could try to address the bias associated with downscaling the climate data by using an approach as the one here:
i) Roberts, DR, Wood, WH, Marshall, SJ. Assessments of downscaled climate data with a high-resolution weather station network reveal consistent but predictable bias. Int J Climatol. 2019; 39: 3091– 3103. https://doi.org/10.1002/joc.6005

8. You provide no maps of susceptibility, which could help in identifying the locations of impending occurrences in a spatial way. Could you please add some of the maps (supplementary material as well)?

My recommendation is that you try to answer all reviewers’ questions on a point-to-point basis in a separate document, and modify the original manuscript accordingly, as well. Please also try to reply to the above questions and suggestions. Please be aware that direct replies in the online discussion web pages are not considered as a response to the decision process, as the latter requires direct upload of the said documents in the manuscript submission and revision page of the journal.
After you submit the modified version and the response letter, the revision process will continue directly to the next review round.

Thank you for submitting to NHESS and, again, my apologies for the delay.
Best regards
FC

dear authors

I am glad to inform you that, after the modifications to the manuscript, the paper is now acceptable for publication in NHESS.
Please follow the forthcoming editorial indications as they will be communicated through the email channel.
Thanks for submitting to NHESS,
best regards
FC