Droughts in western Central Europe and associated atmospheric circulation patterns since 1844

Neimry, Emile; Goosse, Hugues; Jonard, Mathieu

doi:10.5194/egusphere-2025-5490

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https://doi.org/10.5194/egusphere-2025-5490

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02 Dec 2025

| 02 Dec 2025

Droughts in western Central Europe and associated atmospheric circulation patterns since 1844

Emile Neimry, Hugues Goosse, and Mathieu Jonard

Abstract. Droughts in western Central Europe have major impacts on agriculture, ecosystems, and society, yet their long-term variability and drivers remain poorly understood. This study investigates drought variability over the past 180 years and its link to atmospheric circulation to identify their dynamic drivers. Three reanalysis datasets (ERA5, 20CRv3, and ModE-RA) are used to detect meteorological drought events via the 3-month Standardized Precipitation (Evapotranspiration) Indexes (SPI-3 & SPEI-3) and to connect them to atmospheric circulation patterns through k-means clustering. Dataset reliability is assessed over western Central Europe, providing consistent coverage from 70 to 165 years. Results show that recent severe and successive droughts, such as the 2018-drought, have historical precedents and display strong multidecadal variability. Diverging trends between SPI-3 and SPEI-3 over the last decades indicate an increasing role of atmospheric evaporative demand (AED). Summer dryness has intensified over the past 180 years, whereas winter dryness has declined. Regional and seasonal contrasts further emphasize the complexity of drought dynamics. Four distinct circulation patterns associated with droughts are identified: the Baltic High, British Isles High, North–South Dipole, and European High. Over time, droughts have become increasingly linked to the European High, a pattern characterized by strong AED anomalies, intense droughts, and this had a central role in the recent spring drying. The findings highlight the recent emergence of circulation patterns that enhance AED, marking a shift in the dynamic drivers of regional droughts under climate change.

Received: 05 Nov 2025 – Discussion started: 02 Dec 2025

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Emile Neimry, Hugues Goosse, and Mathieu Jonard

Status: final response (author comments only)

RC1:
'Comment on egusphere-2025-5490', Anonymous Referee #1, 03 Feb 2026
Neimry et al. have analysed historical drought trends in western Central Europe (BE,NL,LUX,partFR,partDE). Using three different reanalysis products and four meteorological stations, they calculate SPI3 and SPEI3 and analyse trends and circulation associated with drought events. I find parts of the study interesting, for example the circulation drivers, the long historical perspective and the decadal variability in drought occurrence shown in Figure 5. However, I sometimes got lost in reading the results sections where many statistics are noted and discussed, often in a very lengthy way.
My first point of advise to the authors is thus to make an attempt at shortening the manuscript, by being more direct or where possible leaving out some explanations or moving things to the supplementary materials. An example, at the start of your data section:
“In addition to the identification of droughts, an objective is to connect them to atmospheric circulation. Consequently, reanalyses are pivotal datasets for this study. A reanalysis is a combination of historical weather observations and weather forecasting models, using a method called data assimilation (Slivinski et al., 2019; Valler et al., 2024). This approach provides a comprehensive dataset that represents fields consistent with each other through the application of physical laws within the model and the elimination of spatial and temporal gaps.”
In my opinion this whole bit can go. The objectives are already listed in the introduction, and I think the readers of WCD can be expected to know what reanalyses datasets are. Hopefully by critically assessing the text as a whole, you will be able to achieve a more streamlined and clear manuscript, that better showcases your work and results.
My second point is a hesitation regarding the fact that all analysis (outside Fig 8a/b) is related to SPI or SPEI trends/values, rather than the actual physical input variables of precipitation, temperature and evapotranspiration. I struggle with terms like ‘dryness’, when SPI/SPEI indicate a relative position on the wet/dry spectrum. Please consider performing or adding (in the SI?) Some analysis on the physical variables, and using the language to go with that (related to my point on clarity before). E.g. I believe that when you write about a ‘decline in dryness’ measured by SPI, you in fact mean an increasing precipitation trend. As a physically trained climate scientist that is easier to interpret.
Finally, I miss a critical evaluation in the discussion of the validity of your results and assumptions. Throughout the results section there are quite a few places where the three reanalysis products thoroughly disagree with each other, even when a common period is used. No statements or conclusions are made regarding this, whilst for me this is very important. Furthermore, given your interesting Fig. 5, should you not comment more on the influence of interannual/multidecadal variability on trends in relation to climate change trends (signal to noise discussion)? In a similar matter, I miss a critical discussion on the use of Thornthwaite, which as far as I know is a dated method of calculation PET. I fully understand that when only monthly data is available, this is your only option, but this will influence your computed trends and results, so I expect a critical discussion of implications.
As such, my advise to the editor is to offer the authors a chance to reword, and consider the manuscript again after major revisions have been made.
Major comments
I’m based in the Netherlands, a prominent part of your region of interest. I’m most familiar with the Dutch literature on drought, and these are often missing in your reference list. I list some, as a suggestion to extend your literature review, note this is not a complete list. These are a mix of trend analyses, event analyses and future projections: Philip et al. (2020), Zscheischler et al. (2020), Huang et al. (2021), Van der Wiel et al. (2021), Mens et al. (2022), Aalbers et al. (2023).

Line 157: I’m confused how one can reliably add detail to datasets by interpolation, as you do by upscaling from 1.8 degree resolution to 0.25 degree resolution. Have you verified what the resulting (spatial) fields look like?

Why have you chosen to use ERA5-land rather than normal ERA5? You don’t use any of the land surface variables in your analysis, that would benefit from the additional offline land-model calculations?

Line 306: In your analysis of the quality of the different reanalyses datasets, you fully rely on SPI/SPEI variables which are normalised, derived variables. Please check the reliability of input variables as well, e.g. PR, TAS, PET. Similarly, I think your analyses of trends may be helped by analyses of the input variables rather than SPI/SPEI alone.

Line 205 and 327: Thornthwaite, through its solo dependence on temperature, likely shows a stronger (too strong) trend over the analysis period towards higher PET values. This can somewhat be seen in Figure S1, where the red line has a stronger slope towards low SPEI values (drought). Similarly, in line 327, SPEI-3 drought occur at the end of the time series, whilst SPI-3 droughts occur more at the beginning. Can you analyse, maybe in a similar figure to S1, how the PET differences are, i.e. without the step towards SPEI? Please then also add a point in the discussion on this, and how you think the choice for Thornthwaite has impacted your results (section 7.4 I guess).

Line 403: what does SPI3 minus SPEI3 physically mean, it is a difference between normalised quantities that partially rely on the same input variable. Wouldn’t it make more sense to asses the trend in (normalised if you wish) PET, rather than in this strange quantity?

Section 6.2: This section is very dense with numbers and comparisons, but as a result difficult to follow and distill your main results from. There are a lot of differences mentioned, which are partially attributed to differences (biases?) in reanalysis datasets, but without coming to a conclusion. The influence of multidecadal variability is not mentioned, even though we know (and you showed in Figure 5) that this is of large influence especially for precipitation.

Minor comments:
Line 21: “raising particular concern” while I agree there is concern, I’m not sure it is more concerning in this region then in other regions globally. The Mediterranean, or Sahel, see larger trends towards droughts, transitioning to seasons without significant rainfall. The trends in Central Europe are relatively smaller, the climates relatively wetter and cooler.

Line 45: the transition zone between drier South and wetter North in climate projections is also a prominent feature of the Netherlands national climate scenarios (Van der Wiel et al. 2024).

Line 67: You only use CHDs six times in the manuscript, do you need the abbreviation?

Line 120/Figure 1: Generally I’d advise to avoid using reanalysis-based precipitation products, can you recalculate these values using (for example) the E-OBS gridded observational data? Do mention this limitation in your discussion as well.

Line 157: Please add details on your method to bias adjust to the 1979-2008 climatology, do you remove all trend or calculate the bias in this period and adjust the full dataset according to this bias?

Line 191: I think you have calculated SPI/SPEI at grid point level, and then spatially averaged, but am not 100% sure due to “In order to obtain the SPI-3 and SPEI-3 values for the entire western Central Europe region, precipitation and PET values for all land cells have been averaged over the study area.”, can you clarify?

Line 205: Thornthwaite, through its solo dependence on temperature, likely shows a stronger (too strong) trend over the analysis period towards higher PET values. This can somewhat be seen in Figure S1, where the red line has a stronger slope towards low SPEI values (drought). Can you make a similar figure to S1, just for PET, to quantify this?

Line 225: your reference period for circulation is different from the period for SPI/SPEI calibration, is this done on purpose?

Line 262: “Whilst the aforementioned two variables are hypothesised to remain constant across time for each reanalysis, this is not the case for the former.” I’m not a native speaker, but I don’t think this makes grammatical sense. ‘The former and the latter’, refers to a list of two items, you have three.

Line 272: the Climate Explorer asks to be referred to as a dataset/tool as well, see https://climexp.knmi.nl/citation.cgi.

Figure 3: the SPI reference period is different in the caption than in the main text, the y-label should state SPI-3 [10-year], the figure doesn’t include labels a) and b), I suggest adding to the caption that the shaded area is in the colour of the dataset with the large error relative to the station measurement.

Line 357: why does ERA5 ‘support’ this finding, if it is the only reanalysis in Figure 5 that includes this decade?

Line 362: I’m not sure what you mean with 16th driest conditions here.

Figure 5: please do not use the rainbow colour scale, and please consider having a discrete colour bar (e.g. 10 colours) rather than a continuous, to help reading the figure. The discs are very difficult to see, maybe you can consider a different way of showing those. Just brainstorming: an additional subfigure with a line graph, or larger discs below the map.

Line 369: what is the mean dryness? If it is the mean SPEI3 value, is it not by definition that the two quantities are closely correlated?

Figure 6: a) please replace the rainbow colour scale, consider choosing a white-centred colour scale instead. Why is De Bilt/Frankfurt/Paris not included as a disc? b) I had to zoom in to about 200% to be able to read these graphs, consider modifying such that that is no issue anymore (line width, text size, font weight etc). Maybe all the slope values and p-values can be written in a table?

Line 380: this is slightly silly thing, but SPI or SPEI don’t measure ‘dryness’, they are an indicator of the relative dry or wet state of the past months. As such I don’t think the term ‘dryness trends’ are correct, maybe just (meteo)hydrological trends? Also in caption of section 7.3.

Figure 7: can you add somewhere (maybe in the caption) over how many seasons these were calculated? So for ERA5, 31% of how many droughts are related to the Baltic High? This may give the reader a sense of (statistical) stability.

Figure 8: a) please specify what the box (P25/P75?) and whiskers (min/max?) show, b) why not show this as four bar charts?, c) please replace the rainbow colour map, why not include De Bilt/Paris etc as a disc?

Line 510: “(Fig. 6b, Fig. S8b and Fig. S9b)” should this be Fig 8b?

Figure S11: What are the units on the y-axes? What is shown on the x-axis, are these all identified droughts consecutively? So a modified time axis?

Line 550: what do you mean here by “given the natural variability”? If 1921 was a large outlier, than would we expect an even larger (or slightly smaller if due to PET trends) to occur in the next 100 years?

Line 551: maybe add some literature on multi-year droughts in the region? Van der Wiel et al. (2023) or Van Mourik et al. (2025).

Line 565: “Some studies have been linking” should probably be “Some studies have linked”

Line 640: degree symbol missing. Furthermore, is CC scaling relevant to year-round precipitation? I always assumed it was mostly associated with convective summer rain events, rather than all precip year round? In the KNMI23 projections, extreme precip sometimes goes beyond CC (Van Dorland et al. 2023).

Line 735: I think you mean negative precipitation anomalies rather than precipitation deficits Deficits have been defined in many different ways in the literature so might cause confusion. (Also please check for other places in the manuscript.)

Line 746: Maybe add to this paragraph something short on temporally compounding or multi-year droughts?

References
Aalbers, Emma E., et al. "The 2018 west-central European drought projected in a warmer climate: how much drier can it get?." Natural Hazards and Earth System Sciences 23.5 (2023): 1921-1946.

Huang, Yafei, et al. "Long-term trends in agricultural droughts over Netherlands and Germany: how extreme was the year 2018?." Hydrology and Earth System Sciences Discussions 2021 (2021): 1-27.

Mens, Marjolein, et al. "Integrated drought risk assessment to support adaptive policy making in the Netherlands." Natural Hazards and Earth System Sciences Discussions 2022 (2022): 1-24.

Philip, Sjoukje Y., et al. "Regional differentiation in climate change induced drought trends in the Netherlands." Environmental Research Letters 15.9 (2020): 094081.

Van Dorland, R., et al. "KNMI national climate scenarios 2023 for the Netherlands." Report WR23-02, KNMI 605 (2023).

van Mourik, Jonna, et al. "Regional drivers and characteristics of multi-year droughts." Weather and Climate Extremes 48 (2025): 100748.

Van der Wiel, Karin, Geert Lenderink, and Hylke de Vries. "Physical storylines of future European drought events like 2018 based on ensemble climate modelling." Weather and Climate Extremes 33 (2021): 100350.

Van der Wiel, Karin, et al. "KNMI'23 climate scenarios for the Netherlands: storyline scenarios of regional climate change." Earth's Future 12.2 (2024): e2023EF003983.

Zscheischler, Jakob, and Erich M. Fischer. "The record-breaking compound hot and dry 2018 growing season in Germany." Weather and Climate Extremes 29 (2020): 100270.
Citation: https://doi.org/10.5194/egusphere-2025-5490-RC1
RC2:
'Comment on egusphere-2025-5490', Anonymous Referee #2, 13 Apr 2026
Summary
The author team investigates drought variability in western Central Europe since 1844 using different reanalysis products. Thereby, they focus on links between drought conditions and atmospheric circulation to identify potential drivers of drought. They further employ 2 drought measures. The results show that recent extreme droughts have historical precedents, illustrating strong multidecadal variability of drought conditions in western Central Europe. Assessing the trend behavior of droughts, the author team find seasonal differences as well as differences between the two drought indices. The authors hypothesize that the diverging trends of the two drought indices sued are indicative to an increasing role of atmospheric evaporative demand (AED). They back this hypothesis with changes in circulation patterns towards AED prone patterns highlighting the role of dynamics over thermodynamics in this region.

General statement
The author team certainly touches interesting questions on whether recent droughts are unprecedented and more importantly how drivers of drought in in western Central Europe have changed over time and what is the role of atmospheric dynamics versus thermodynamics. In this sense the study is of high importance and deserves publication in WCD. Still, in the current form there are major concerns, the structure of the manuscript needs to be improved, the manuscript needs substantial shortening, the atmospheric circulation classification needs better explanation, and the discussion needs more focus. So, I recommend acceptance after major revisions.

Major comments
Structural problems concern the data section, not containing a description of all data sets used (see minor comments), method section (4.1) not explaining PET when it is introduced the first time, and parts of the results belong to the evaluation section.

Method section 4.3: I do not understand how the authors classify atmospheric circulation. It remains unclear to which variable the k-means clustering is applied, is it applied to drought indices as somehow written in paragraph 2 or to Z500?

The discussion is not focused and far to long, see my suggestions below.

Minor comments
Throughout the manuscript: The authors do often use the wrong tense, So I suggest using present tense throughout the manuscript except you actively write that person A showed something (example: Paulin et al. (2006= suggested that …), then use past tense. Avoid using form like “have attracted” or “is becoming”.
L40: Please add the publications https://doi.org/10.5194/cp-19-2511-2023 and https://doi.org/10.5194/cp-17-887-2021 by Kim dealing with Med droughts during the last Millenium.
L43: There is a study assessing a lot of different drought indices over Europe which might be of interest to the authors: Raible, C. C., O. Baerenbold, and J. J. Gomez-Navarro, 2017: Drought indices revisited - Improving and Testing of Drought Indices in a Simulation of the last two Millennia for Europe. Tellus, 69, 1296226
L45: The sentence reads bad, please reformulate.
L51-53: The sentence reads bad, please reformulate.
L58-59: Maybe change to “circulation dynamic is hypothesized to prevail ... land-atmosphere feedback dominate ...”
L59-62: Againthe sentence needs to be improved.
L75-76: Please add publication https://doi.org/10.5194/cp-19-2511-2023 here.
L94: “it is imperative” reads bad, please change.
L103-105: It is weird that section 5 is introduced after section 6.1 and 6.2
L116: Which alpine influence have the authors in mind?
L122: I would not say that Western Central Europe is predominately forested, as over 63% is not forested. So please reformulate.
L140-153: Please state which data assimilation schemes are used in these different reanalysis products.
Section Data: It remains unclear which time resolution is used. I guess it is monthly not is it not stated clearly in this section.
Section Data: I suggest to also introduce the observational station data in this section, so move the paragraph on line 271 and the table 2 to this section.
L156: Change “In order to” to “To” throughout the manuscript.
L157: What is bilinear downscaling? For me it sounds like the authors just applied a bilinear interpolation where no new information is added (statistical downscaling adds sub-grid information from observations and dynamical downscaling runs a model in higher resolution adding additional resolved processes). I think this is a bit problematic as a reanalysis produced on a coarse grid like 1.8 degree never will the fine scale processes, so normally, if the authors would like to compare the data sets with each other one should interpolate the finer resolved data sets to the coarse resolution to make a fair comparison. At least the authors need to discuss this issue in the manuscript.
L173: Please move the used PET estimation here, which is mentioned at the end of this subsection.
L175: How is AED calculated?
L192-194: You do not need to explain area weighted, so you can change to “... precipitation and PET values for land cells are area weighted. For each month ...”
Fig.2: I think this figure is not necessary, it might be useful in a PhD thesis but for a paper the description in the manuscript is sufficient.
L205-208: Please move to L 174.
238: What is AIC und BIC?
262: replace “two variables” with features
L281-291: This paragraph remains unclear: I would rather introduce the metrics in the method section and only discuss the results in this chapter
Fig. 3: a) and b) are missing.
L319: I suggest making a kind of introduction of what will be presented in the results section.
L321-324: This part belongs to the evaluation.
L341-42: Do not make sentences which refer to a coming section, If you discuss this in section 6.2 then make a reference to section 6.1
L350: what are the two drought types here, and how are they measured?
Fig.5: do not use rainbow colour scales, it is unreadable for colour blind people (15 % of the word population)
L384: This should be Fig 6b. Correct?
L401: This should be Fig 6b. Correct?
Fig 6b is discussed before Fig 6a, which is almost not discussed.
Fig.6a: do not use rainbow colour scales
L460: Both clustering, what is meant here?
L505: These are just “modes or variability”
L515: How is high confidence defined?
Fg. 8d do not use rainbow colour scales
L537-553: What is the message of this paragraph?
L564: Given the low frequency variability of AMO (around 60 yr periodicity) a correlation of -0.3 to –0.68 is not impressive and rather significant over the ERA5 time scale of 70 years, I would argue that this is just noise. So, reformulate or remove this paragraph
L574-589: Well, you selected the area in a way that it shows a uniform behaviour so no wonder that there is a high correlation. This paragraph can be removed.
Section 7.2 and 7.3, What is the difference between drivers of trends and dynamics behind the droughts. I thought you discuss the driver and say which drivers re responsible for which trends. So, my suggestion is to merge these sections and shorten it to the main findings of the paper wrt existing literature.
Section 7.4: I suggest removing this section and may include 1-2 sentences of challenges in the conclusions.
L769: Which processes are meant here?
Citation: https://doi.org/10.5194/egusphere-2025-5490-RC2
RC3:
'Comment on egusphere-2025-5490', Anonymous Referee #3, 15 Apr 2026
Weather and Climate Dynamics (WCD)
Droughts in western Central Europe and associated atmospheric circulation patterns since 1844
Summary:
The authors conduct a study about droughts in western Europe with the aim of comparing recent events with a historical context, and show possible weather patterns associated with drought events. Although previous literature has highlighted the weather patterns associated with past European hydroclimate extremes (Brönnimann et al., 2025), this study focuses on a historical perspective on a specific region of Europe (needed for more local management). They used three reanalyses to have a reliable multi-evidence approach (plus ERA5-Land, which is only used for two figures and not in the result section). The study includes the validation of the three reanalyses against four stations and their use for calculating the SPI and SPEI indices. They show important events in the last ca. 175 years and conduct a clustering of weather patterns and modes of variability that then relate to periods of drought. They extensively discuss the patterns associated with droughts.
General comment:
It is necessary to unravel the different factors modulating droughts in the important socio-economic region (western central Europe). The manuscript shows a huge amount of work that is relatively well-connected and coherent. However, the manuscript is extremely long, so much, that it is difficult to remember the structure/sections and procedures described in the manuscript (a copy-edited version would be a 30-page article, way too long). The writing style is very wordy; one can say the same in fewer words, mainly using the active voice. Moreover, there are calculations and representations (plotting) that are methodologically questionable. There are many basic explanations, in my opinion, for a reader of WCD; on the attached PDF, I suggest or give examples of what can be modified (155 comments, including section 5). I recommend that the authors make several major adjustments to the manuscript, adjustments that need to be evaluated again before acceptance.
The manuscript shows four circulation patterns and discusses some possible dynamics (variables) related to their influence on droughts. However, my major concern is that the form in which the authors tackle the relationship between the weather patterns and droughts does not specify the mechanisms of drought development. That is, showing other specific variables could be necessary to elucidate what makes the drought evolve. For instance, how do the circulation patterns affect moisture transport, convergence, clouds, and radiation? How do these variables – under the circulation patterns – interact with soil moisture in previous months?. At the moment, it is only discussed by mentioning the influence of atmospheric subsidence with cloud-free skies or advection of warm air masses on evaporation. The aforementioned variables are important for the temporal development of the drought.
Other major concerns are the description and implementation of some procedures:
There are two reference periods: one for the drought indices (1947-2008) and the other for the Z500 anomalies clustering (1979-2008); this is very confusing for the reader (lines 190 and 225).

Moreover, the SPI and SPEI indices at 3-month accumulation are the underpinning of the study, but then they specify that the indices are calculated grid-wise and also with the average of the region (line 191). Moreover, the authors calculate a difference between both indices that represent different dynamics: one, the standardised rainfall variability, the other the standardised water balance variability. What is the physical meaning of that difference? (Line 403).

The use of Thornthwaite instead of a more physically based PET is detrimental if one wants to analyse the influence of the increase in temperature in droughts (as reiterated throughout the discussion about the Atmospheric Evaporative Demand, AED).

Moreover, the authors also plot the 10-year moving average indices – which is strange for indices which are already accumulated – with the trends on top, what is the aim with that combination? Likewise, plotting all four seasons and the whole series with transparency makes the plots almost impenetrable to the eye; plotting so many panels and the long time series also does not help.

Furthermore, the procedure to create Figure 8a is not described, and the figure is very confusing itself (the authors try to link the four weather patterns with major modes of variability – “three first EOFs”); the y-axis says index coefficient (the SPI or SPEI?), but apparently is related to the EOFs, not the SPI or SPEI. This figure is essential for the discussion and conclusions of the weather patterns and thus the conclusions of the manuscript.

The discussion is also very long and deviates to other topics rather than comparing the drought dynamics identified by other studies. The conclusions have a proper length, but they only refer to the European high weather pattern with some specificity; the other patterns are only mentioned without specifying how they influence the development of droughts. Conclusions also only try to make a summary; they could connect the relationship between the weather patterns and the modes of climate variability or how they would evolve in the future (for the discussion section as well), as a strong focus of the manuscript is the analysis of trends.
Many of the concerns should also be addressed by adhering to the FAIR principle. That is, the authors should make their codes available for reproducibility!
The reference to several subplots in the text is disorganised. Almost all subplots do not have an individual letter to identify them and are also not referenced in the figure description in the results text section (not only in the caption). The size of the labels in the plots is way too small.
Specific comments:
I attach the PDF file with my comments where I make specific suggestions on some sentences, ask questions about procedures or request better explanations. In the following, I specify the most important ones.
Line 104 - It is debatable if Section 5 (datasets evaluation) should go in the main text of the manuscript. It can be shown as supplementary material. This evaluation, although important, diverts the attention of the reader. It adds too much information, making the manuscript very long. This will shorten the manuscript or give space for deeper analysis.
Line 115 – The argument for choosing the region of study does not seem relevant. There are other very strong arguments, see the comments on the PDF.
Line 128 - Figure 1 can be found in an Atlas; then what is its significance in this long manuscript? Probably can be added to the supplementary material for the curious reader.
Line 170 – Figure 2 is not necessary; it can be consulted elsewhere and readers of WCD most certainly know the meaning.
Line 191 - If the indices were calculated by cell, what do you then average the inputs for the indices in the whole area? Did you recalculate the indices? why the averaged inputs? Were the indices calculated twice? If you average the indices calculated on a grid, it should give the same value of the indices calculated with the average of the gridded Ppt and ETP. The indices only make a normalisation.
Line 205 and 208 - The Thornthwaite PET. If Penman-Monteith was calculated (as shown in Figure S1), why don't they use and report the most accurate formulation? This is very concerning as Thornthwaite is heavily influenced by changes in temperature.
Line 236 - There are previous research that identified 7 patterns, and recently identified 9 (CAP7 and CAP9; Schwander et al., 2017; Pfister et al., 2025). If the number 4 was identified with some criteria based on intermediate results, it should be better explained.
Line 242 – The EOF results are mentioned in the methods section. Then the authors decide to assign a name – I assume to the first three EOFs – but you don’t show the variance explained, not even in the results section, Figure 8a, where you come back to them. Either the authors placed it in the supplementary material, or they only say in the methods that you used EOFs and show everything in the results section.
Line 270 – No, ERA5-Land is a land surface simulation, meteorologically forced with downscaled estimates from ERA5. This should be better described and understood. Moreover, you only show ERA5-Land in the evaluation section and in Fig 1, then you never use it again. This is not consistent.
Line 288 and 300 - Fig 3a shows the SPI, not the MAE, or the y-axis is wrongly labelled. Also, there are several panels. If 3a refers to the left panels, then the y-axis is wrong. If it was related to the evaluation using the MAE, then you must show the time series of the moving MAE, not the 10-year moving SPI index.
Line 322 – Why were Matthew’s Correlation Coefficient used to compare the three reanalyses? This seems like an evaluation and not the results of the drought dynamics and their drivers, which are the aim of the manuscript.
Line 350 – drought types or drought indices?
Line 384, 437 – Why do the maps only show ERA5 if you are trying to make a historical analysis with the other two reanalyses? The line talks about the three reanalyses; it is better to talk about the recent trends and reference Figure 6a, which only shows ERA5. Then, in another sentence, you talk about the other reanalyses and reference specific panels in Figure 6b.
Line 460 - Is not the clustering calculated using the Z500 (line 225)? Why do you refer to SPEI clustering? Is it “clustering of Z500 based on the SPEI-identified droughts”?
Line 475 - The weather patterns based on clustering of the Z500 were also based on the identification of droughts with a particular index. But here it is not specified which of those indices. It was also not properly described in paragraph Line 216 of the methods section.
Line 489, 520 – The y-axis (“count”) of Fig 8c does not match the number of squares and the figure is not explained, only called “waffle chart”. Also Fig 8c is described first than 8a.
Line 496, 497, 518 – Fig 8a supposedly is created based on SPEI but then the authors refer to an index of 0.09 for both SPEI and SPI. This is very confusing because, first, that is not a threshold for identifying the droughts, and second, the figure should be based on the clustering that is based on one drought index (according to the methods). If the “index” in the y-axis of the figure refers to the EOFs time series (it can also be called the PCAs time series or just PCA), then the analysis in the text is wrong, or vice versa. This needs to be corrected because it is essential for the discussion and conclusions of the weather patterns, and thus the conclusions of the manuscript.
Line 519 – drought intensity anomaly: anomaly of the drought values? So, the authors calculated a climatology only considering the drought events? This is very wrong. Why are there positive values for a drought? The SPI and SPEI are already expressed as a standardised anomaly.
Line 552 - In the manuscript, the authors don't define the intensity as severity (summatory of duration and value of the index - SPI or SPEI). Then the authors should say more explicitly what they mean by intensity (we understand the values of the index).
Line 596 – If the manuscript is about droughts, why discuss the wet periods? Considering that the manuscript is very long, this sentence of discussion only makes the manuscript unfocused.
Line 739 – Section 7.4 should be better called "challenges for drought management". Otherwise, the reader interprets it as possible future research.
792 – The only weather pattern that is concluded about is the European High. For sure, the other patterns must have something to highlight about.
Probably citing the same article many times is making the introduction too long as well.

Technical comments:
Attached file with more in-line comments.
Citation: https://doi.org/10.5194/egusphere-2025-5490-RC3
EC1:
'Co-editor comment on egusphere-2025-5490', Heini Wernli, 15 Apr 2026
Dear Emile Neimry and co-authors
As you have seen you received three detailed reviews of your manuscript. While all reviewers see great value in your study, they also have many critical comments. They all highlight the need to shorten and better focus the paper and to clarify important element of your analyses. Important recommendations from the reviews are:
paper is lengthy and readers get lost in the details --> shorten the paper and focus on key novelties

avoid unnecessary repetitions

include discussion of limitations of your study

better explain the circulation classification

better motivate how and why you use SPI and SPEI and related metrics (in contrast to one reviewer, I think it is good that you introduce SPI etc. in the main part of the paper, not all WCD readers are familiar with these metrics)

improve the discussion of the figures (using labels to specific panels etc.)

please also address the many constructive and detailed comments

in general do your best to improve language / grammar

Please take your time to do the required major revisions. Note that in the first step you have to upload the "final author comments". This is not yet the revised papers. This comes then in step 2, where you can prepare the revised version and a more detailed reply document.
With best regards,
Heini Wernli
Citation: https://doi.org/10.5194/egusphere-2025-5490-EC1

Emile Neimry, Hugues Goosse, and Mathieu Jonard

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https://doi.org/10.5194/egusphere-2025-5490-supplement

Emile Neimry, Hugues Goosse, and Mathieu Jonard

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Cumulative views and downloads (calculated since 02 Dec 2025)

Month	HTML	PDF	XML	Total
Dec 2025	115	90	16	221
Jan 2026	84	79	4	167
Feb 2026	55	64	6	125
Mar 2026	34	51	4	89
Apr 2026	84	114	12	210

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Short summary

This study examines 180 years of drought variability in western Central Europe and its links to atmospheric circulation using three evaluated reanalysis datasets. Results reveal strong multidecadal variability, winter wetting, summer drying, and a growing influence of atmospheric evaporative demand. Four circulation patterns are identified, with recent droughts increasingly tied to the European High, marking a shift toward dynamics that intensify drought under climate change.


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