The paper "ESD Ideas: Translating historical extreme weather events into a warmer world" proposes a new approach to examining how extreme weather events may differ in a warmer or cooler world using a reanalysis-based approach. This approach involves producing reconstructions of events as they occurred and in a counter-factual warmer or cooler climate to compare the consequences. The authors use the NOAA-CIRES-DOE 20th Century Reanalysis v3 system, which assimilates surface pressure observations into a NOAA weather forecast model to reconstruct the atmospheric circulation from 1806-2015. As a proof-of-concept, the authors translated the reanalysis of a severe windstorm that occurred in February 1903 into a warmer world where it produces higher wind speeds and increased rainfall. 

The paper suggests that this approach offers a novel way to develop plausible storylines for some types of extreme events that other methods may not be suitable for. The paper also discusses the different methodologies that exist to understand how climate change has affected extreme events, such as risk-based approaches and storyline approaches, and how event storyline approaches attempt to quantify how an extreme event would be different in an altered climate. 

Overall, the paper presents an innovative and interesting approach to examining extreme weather events in a changing climate. While I really appreciate the purpose of the proof of concept, the following points should be discussed in details in the revisions:

-Forgotten Similar Approaches: there has been a recent attention on storyline approaches focused on analogues that look quite similar to those proposed here, as they make use of sea-level pressure patterns and seek for spatial changes of extratropical storms intensity: "A framework for attributing explosive cyclones to climate change: the case study of Alex storm 2020" by Ginesta et al. (2022, Climate Dynamics): This paper focuses on attributing the storm Alex, an explosive extratropical cyclone that hit Southern France and Northern Italy in October 2020, to climate change. The authors use the analogues method on sea-level pressure maps to identify 30 cyclones that match the dynamical structure of Alex for two periods: 1950-1985 and 1985-2021. They find that in the factual period, the anticyclonic circulation over the North Atlantic and the cyclonic circulation over Northern Africa are deeper than in the counterfactual period, and storms like Alex are more persistent and more predictable in present-like conditions. The authors also track the analogues and find that under current conditions patterns like Alex are more wavy than in the past."Climate-change attribution retrospective of some impactful weather extremes of 2021" by Faranda et al. (2022 WCD): This paper addresses the question of whether climate change may have affected the characteristics of specific extreme events that occurred in 2021 over Europe and North America. The authors use the ERA5 dataset from 1950 to the present to define present (factual world) and past climate conditions (counterfactual world), and they identify the most similar sea-level pressure patterns to the extreme events of interest in each period. Other approaches that are similar but do not require to run simulations are those via stochastic weather generators introduced by Yiou, P.: AnaWEGE: a weather generator based on analogues of atmospheric circulation, Geosci. Model Dev., 7, 531-543, 2014. I think that these approaches should be mentioned at least as an alternatives of running evey time ad-hoc simulations of each events.

-Limited scope: The paper only presents a proof-of-concept analysis of a single extreme weather event, and the approach has not been applied to a wide range of events. Therefore, the generalizability of the approach is still unknown, and further research is needed to validate its usefulness across a broader range of events.

-Assumptions: The approach relies on a number of assumptions, such as the uniformity of the sea surface temperature perturbation and the absence of other perturbations given by internal variability of the climate system. These assumptions may not hold true in all cases and could potentially affect the results. For example what would happen if the major source of moisture for the storm relies on an atmospheric river whose origin is somewhere by the tropics ?

-Interpretation: The paper does not provide a detailed discussion of how the results should be interpreted in terms of their policy or societal implications. It is important to consider how these findings could be applied to mitigate the impacts of extreme weather events in the future.

-Uncertainty: While the approach provides a new way to understand the potential impacts of extreme weather events in a warmer world, it does not account for all sources of uncertainty. Therefore, it is important to acknowledge that the results are not deterministic and that there is still significant uncertainty associated with projecting the impacts of climate change on extreme weather events.

My reviews are always intentionally signed because I value an honest and transparent discussion between authors and reviewers.

Davide Faranda, CNRS 

This manuscript investigates how Storm Ulysses (in 1903) would look like if it occurred in present-day climate conditions. The study is based on the latest version of the 20CR reanalysis.

The approach followed by the authors is interesting and promising, but I have some reservations on a few points.

1. The authors present the use of 20CR as a methodology or an approach. In my opinion, using this reanalysis is a way (among others) to construct analogs of a past storm. A "methodology" is the sequence of operations that uses reanalyses (or any other dataset) to produce a result, not the reanalysis itself, which exists independently of extreme event attribution issues.
2. The idea of simulating analogs of observed storms in different climate conditions has already been published (e.g., among others, Ginesta, M., et al., A methodology for attributing severe extratropical cyclones to climate change based on reanalysis data: the case study of storm Alex 2020. Clim Dyn (2022). https://doi.org/10.1007/s00382-022-06565-x), albeit with another reanalysis. 
3. The authors spend many lines reviewing (albeit partially) extreme event attribution, but barely explain how they obtain their figures.

Therefore, my feeling is that the authors present a new (and certainly great) dataset that is useful for extreme event attribution. But I don't see any conceptual advance for the methodologies of extreme event attribution. The authors could consider revising the manuscript by a more thorough bibliographic search, and a clarification of the methodology they actually use.