General comments:

This manuscript is devoted to the analysis of the recent mismatch between the interdecadal variability of storm losses in Europe, estimated from wind data using a conventional approach, and of indices of the large-scale atmospheric circulation (teleconnections), such as NAO or AO. This lack of agreement may have critical implications for insurance companies and the general population, thereby being very pertinent and within the scope of NHESS. A new hemispheric geostrophic index (HGI), based on the 700 hPa geopotential height, is then proposed as an alternative to the more conventional indices, showing a higher correlation with the recent changes in storm losses in Europe. It is argued that HGI, being closely related to the low-tropospheric thickness rather than to near-surface conditions, explains this better correspondence. Although these findings are scientifically sounding, I found some parts a bit too speculative, thus deserving a more accurate assessment and demonstration. The text is concise and clearly written. The quality of the figures can be improved. Some revision suggestions are outlined below. Hence, I recommend the publication of this manuscript after some revisions outlined below in the specific comments.

Specific comments:

Section 2.1: please describe in greater detail the datasets and the quality of the data. The average of the two reanalysis products (ERA5 and reanalysis) is also worth explaining, preferably taking into account previous research.

Section 2.2: the use of 11-yr running means without values at the ends of the time series can also be improved using other more advanced methodologies, such as a low-pass filter with a cut-off frequency at 10 years.

Ln 104: Please specify "...to the present day".

Section 3.1: the limitations of the event loss equation are not stated, including their potential contribution to the recent bias. This is a very important aspect to discuss.

Sections 3.2 and 3.3: an assessment of the statistical significance of the trends and divergence is essential. For instance, the statements in Ln 142-143, 161-162 and 207-208 are very vague and need to be proved using robust statistical analysis of trends and inversion points.

Ln 231-232 seems to contradict the use of a zonal/hemispheric index. Please clarify.

The last paragraph of section 4.2 deserves a better discussion, including a more detailed analysis and discussion. Please revise.

The manuscript deals with a important topic, which is the decadal variability of windstorms affecting Europe, and the different perspectives of meteorology vs. insurance. While the idea is good, the execution has unfortunately many shortcomings, both in terms of the assumptions and analysis, and the reasoning and conclusions are very speculative on some parts. Therefore, I must suggest the rejection of the manuscript in its present form. However, I think that a strongly reworked version could be an important contribution to NHESS. Below you can find a list of the major caveat of this study, and I hope these help the author to reformulate the manuscript

Major points:

1) datasets, line 75ff - it is not understandable to me why the AVERAGE between two different reanalysis is taken here. This will flatten the fields without necessity. Please do two separate analysis, one for each Reanalysis. To look into the relationship between the large-scale patterns / pressure gradients and storms, a sub-monthly time scale would be preferable (e.g. Fink et al., 2009, doi:10.5194/nhess-9-405-2009)

2) same section - it not clear to me why the wind gust variable from ERA5 was not used, but rather the 10m winds, as it is the former that is responsible for the damage.

3) same section - The version perils data used by the author give extremely limited information and are heterogeneous in space an time, thus hampering the analysis. I would recommend to use the commercial version of the data, to which the author should have access to.

4) data processing - this section is badly written and lacks an lot of details, and thus the methodology is not understandable. For example, I do not unterstand how "monthly data is processed into storm seasons", and the quantification of the storms and their impacts must be done at the sub-daily scale, optimally 3h for ERA5.

5) 3.1. basic method. The metholody by Klawa and Ulbrich is well established and has been used by a large number of publications since. However, it is not clear for which area the data is calculated, or many other details

6) same section - Given that it is not clear for which area the index was calculated, and the PERILS data is only available as a single value for a subset of countries affected by a storm, the reasoning regarding Figure 1 is not understandable. A lot is section 3.2. is quite speculative. In particular, the conclusion in line 161 is not justified.

7) section 3.3. it is not understandable for me how the loss variability of a single, small country like the Netherlands can be used to make assumptions for such a large area. Small countries have either a full hit or not hit (and thus a steeper loss curve), while larger countries like Germany or France have often partial hits, leading to a flatter curve (e.g. Karremann et al. 2014,  doi:10.1088/1748-9326/9/12/124016) Thus I totally disagree with the statement in lines 170-171.

8) section 4.1 the variability of the storm activity over Central Europe is partially associated with the mentioned large-scale patters, but is best associated with a eastern shifted NAO pattern (see e.g. Fink et al., 2009). Regarding the NAO pattern I think the author is overstating the results, because a correlation of 0.60 indicates an explained variance of 36% only.

9) I strongly disagree with section 4.2. - to achieve a better skill for storm impacts over Europe, a more specific index should be chosen, like a shifted NAO index, and not an even larger-scale pattern.

10) same section - the author is assuming a linear relationship between winds above the boundary layer and surface gusts, which is a oversimplification as the main factor is actually the turbulence in the boundary layer (see e.g. Born et al., 2012, doi:10.3402/tellusa.v64i0.17471). Thus is particularly true for when strong convective is enbedded in the cold fronts of the storms, see again Kyrill, Fink et al., 2009).

11) Conclusions - given that the analysis has many weaknesses, the conclusions are unfortunately highly speculative.