MORE, a new convection-permitting reanalysis dataset over Italy and Alpine region: Validation and application in weather, climate and hydrology

Stocchi, Paolo; Cavalleri, Francesco; Tariq, Mohsin; Brunetti, Michele; Camici, Stefania; Mastrangelo, Daniele; Di Sante, Fabio; Davolio, Silvio

doi:10.5194/essd-2025-789

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https://doi.org/10.5194/essd-2025-789

Preprints

31 Mar 2026

| 31 Mar 2026

Status: a revised version of this preprint is currently under review for the journal ESSD.

MORE, a new convection-permitting reanalysis dataset over Italy and Alpine region: Validation and application in weather, climate and hydrology

Paolo Stocchi, Francesco Cavalleri, Mohsin Tariq, Michele Brunetti, Stefania Camici, Daniele Mastrangelo, Fabio Di Sante, and Silvio Davolio

Abstract. This study presents a new convection-permitting reanalysis dataset over Italy and Alpine region, produced through dynamical downscaling of ERA5 reanalysis with the non-hydrostatic mesoscale model MOLOCH. MORE (MOloch-downscaled ERA5 REanalysis) is a very high spatial resolution (~1.7 km) gridded dataset, covering the 1990–present period. The dataset includes hourly outputs of a wide range of variables at the surface and on pressure-levels.

MORE validation follows a multiscale framework applied to precipitation and near-surface air temperature using dense and quality-controlled observational datasets. MORE is benchmarked against other convection-permitting products and coarser- resolution reanalyses. Results show that MORE realistically reproduces spatial and temporal variability and improves the simulation of wet-hour frequency, precipitation intensity, sub-daily extremes, particularly during convective regimes, and key climate indicators such as the number of tropical nights, although a systematic cold bias is present in temperature.

As an application example, the May 2023 Emilia-Romagna (northern Italy) catastrophic flood is analyzed. MORE successfully reproduces the meteorological evolution of the two heavy rainfall events, providing added value in the representation of the mesoscale features resulting in localized precipitation extremes. In cascade, hydrological simulations driven by MORE data improve the representation of catchment-scale discharge, and soil moisture dynamics.

Overall, MORE represents the highest-resolution convection-permitting reanalysis currently available for Italy and the Alpine region. Its comprehensive set of variables at hourly resolution makes it a valuable reference for hydrometeorological studies, climate change adaptation, and climate services in regions with complex terrain and high exposure to extremes. The dataset is openly available at DOI: https://doi.org/10.5281/zenodo.18470948 (Stocchi, P. 2026) and will be periodically updated to ensure long-term accessibility, reliability and completeness.

Received: 16 Dec 2025 – Discussion started: 31 Mar 2026

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Paolo Stocchi, Francesco Cavalleri, Mohsin Tariq, Michele Brunetti, Stefania Camici, Daniele Mastrangelo, Fabio Di Sante, and Silvio Davolio

Status: final response (author comments only)

RC1:
'Comment on essd-2025-789', Anonymous Referee #1, 09 May 2026

The manuscript presents a new hindcast over Italy and the Alpine region, obtained through dynamical downscaling of ERA5 using the MOLOCH model. I use the term hindcast to distinguish this product from conventional reanalyses. I am impressed by the breadth and quality of the work carried out by the authors. The datasets employed meet the standards required for state-of-the-art climate science, the dynamical downscaling methodology is well-established and thoroughly tested, and the numerical model itself has a solid track record in the literature. The authors are to be commended for ensuring the dataset is fully traceable and openly accessible. The paper is dense and rich in results. Perhaps, more so than is typical for a single publication. Upon reading it, I was left with the impression that the manuscript could sustain more than one paper. That said, I do not question the authors' decision to present their hindcast alongside extensive evaluation and illustrative applications, as this provides a comprehensive reference for the community.

In my view, the primary limitation of the manuscript in its current form lies in the clarity of its communication, as detailed in the comments below. I therefore recommend publication of the manuscript, provided the authors adequately address the remarks that follow.
Comments:
1. Motivations and originality. In the Introduction, you note that several other deterministic reanalyses/hindcasts already exist over the same domain, at resolutions that are not substantially different from that of MORE. Yet in the Conclusions, you characterise MORE as a major achievement. Intuitively, I would associate a truly major breakthrough with a product offering a qualitatively new capability e.g. such as a convection-permitting ensemble hindcast, to give one example. This raises a question that I believe deserves more explicit attention: what motivated the development of yet another deterministic hindcast over this domain?

I would encourage the authors to revisit and strengthen the Introduction accordingly. Several angles could be worth exploring: Is the novelty primarily associated with the use of MOLOCH, which may be better suited than other models to specific weather regimes or orographic settings relevant to the Alpine region? Is there value in contributing an additional member to a growing multi-model ensemble of regional reanalyses, thereby benefiting the broader community? Is there coordination or an established dialogue with other hindcast producers over this domain? Or is the primary driver a more practical one: namely, the need for an in-house product to support bias adjustment of regional climate projections?

A clear and candid articulation of the authors' motivations in the Introduction would greatly benefit the reader, and would at the same time allow a more precise assessment of the originality and contribution of this work.
2. Verification scope. There is a clear imbalance between the number of variables produced and delivered by MORE and the two variables selected for verification. I acknowledge that soil properties are implicitly evaluated through the application sections. In general, I agree that 2-metre temperature (t2m) and total precipitation (tp) are likely to be the most widely used variables among the target user community, and that they benefit from relatively dense observational networks, making observation-based verification straightforward. However, in its current form, the manuscript does not clearly articulate the rationale behind this choice, nor does it explain why upper-level atmospheric variables are excluded from the verification exercise. A comparison between ERA5 and MORE at upper levels would, for instance, have been an informative addition. I am not asking the authors to perform such additional analysis, but rather to provide a transparent justification, either in the Introduction or in the Methods/Evaluation section, for the decision to focus exclusively on surface variables. Where relevant, the authors could also direct readers to existing literature in which MOLOCH has been evaluated against other variables (e.g. wind, pressure, upper-level temperature), thereby contextualising the verification choices made here and reassuring readers that the model's performance for those variables has been assessed elsewhere.
3. Communication of evaluation results. I have some uncertainty regarding the overarching motivation behind the evaluation as presented. In places, the text and its interpretations read as a sanity check of the MOLOCH downscaling. This is a legitimate goal, but the suitability of MOLOCH for dynamical downscaling is already well supported by prior literature. I would argue that the most important message of the evaluation is currently underemphasised, and that the text would benefit from revision to remove repetitions (e.g. the concepts in lines 711-714 are repeated several times across the manuscript) and improve readability.

In my view, the central contribution of the evaluation should be to give prospective users a concrete sense of the accuracy and precision of MORE for the verified variables at the temporal aggregations considered. Specifically, I would encourage the authors to address the following questions explicitly: What is the typical uncertainty of daily precipitation estimates from MORE, expressed, for instance, as a relative error? How does this change at hourly aggregation? What uncertainty should a user expect when working with 2-metre temperature? The answers to these questions are arguably the most practically valuable outcome of the evaluation, yet they are not sufficiently prominent in the current manuscript.

Equally important is guidance on the appropriate use of MORE: for which types of analysis is the product recommended? Are there specific regions, seasons, weather regimes, or applications for which MORE is known to perform less reliably, and where its use should therefore be approached with caution? This information, both the quantified uncertainty estimates and the user guidance, should be given a much more prominent role in the paper, and should be reflected clearly in the Conclusions and the Abstract.
4. Lines 113-119. The statement on "ensemble approaches" is not clear. Consider moving this paragraph to Conclusions?
5. Lines 211 and following paragraphs. The manuscript would benefit from a more explicit statement of the objectives underlying the two distinct strands of the evaluation. What insights do the authors expect to gain from comparing MORE against observations? And what is the purpose of comparing MORE against other reanalyses? I believe the answers are implicit in the text, but leaving them for the reader to infer is an unnecessary source of ambiguity. Once again, I would encourage the authors to state these motivations clearly and upfront. For instance, framing the comparison against observations as an assessment of the accuracy and precision of MORE, and framing the comparison against other reanalyses as a sanity check confirming that the downscaling procedure does not introduce gross inconsistencies relative to established products.

On a related point, there is a degree of internal tension in the manuscript: the authors state that they do not intend to rank the reanalyses or hindcasts (line 308) under comparison, yet the structure and language of the evaluation section largely does exactly that. I would ask the authors to resolve this inconsistency. If the comparison with other reanalyses is intended as a sanity check rather than a performance ranking, then the criteria for a satisfactory outcome should be made explicit. What would a successful sanity check look like? What level of agreement, or disagreement, with other reanalyses would the authors consider acceptable, and on what basis? Providing clear answers to these questions would considerably improve the interpretability of this section for the reader.
6. If the comparison with other reanalyses is intended as a sanity check, upscaling MORE to the coarser reference grid would be a more methodologically appropriate basis for the intercomparison. Evaluating global reanalyses on the native high-resolution grid of a convection-permitting product places them in an inherently unfavourable position (lines 398-402). That said, such a comparison may carry practical value by illustrating quantitatively what is lost by forgoing dynamical downscaling, and may discourage the uncritical use of global reanalyses for applications requiring high spatial detail. If this is part of the authors' intent, I would encourage them to state it explicitly.
Line 251. How do you remap? Bilinear interpolation?
Figure 4. Check the P99.9. Should it be P99?
Figure 13. The use of dots renders the figure somewhat difficult to interpret. The authors may wish to consider replacing dots with lines, which would sacrifice some precision in the representation of individual values but would likely improve the readability of the distributional tails considerably.
Figure 14. Change MOLOCH-ISAC into MORE.

Citation: https://doi.org/10.5194/essd-2025-789-RC1
- AC1: 'Reply on RC1', Paolo Stocchi, 01 Jun 2026
  
  We thank the reviewer for the careful reading of the manuscript and the constructive comments. Below we provide a point-by-point response.
  Comment 1: Motivations and originality. In the Introduction, you note that several other deterministic reanalyses/hindcasts already exist over the same domain, at resolutions that are not substantially different from that of MORE. Yet in the Conclusions, you characterise MORE as a major achievement. Intuitively, I would associate a truly major breakthrough with a product offering a qualitatively new capability e.g. such as a convection-permitting ensemble hindcast, to give one example. This raises a question that I believe deserves more explicit attention: what motivated the development of yet another deterministic hindcast over this domain?
  Reply 1: We thank the reviewer for this important comment. We agree that MORE should not be presented as a fundamentally new modelling capability or as a replacement for existing regional reanalyses and hindcasts. Rather, its value lies in providing an additional independently generated convection-permitting dataset over Italy and the Alpine region, based on a different modelling framework and downscaling strategy than existing products.The motivation for developing MORE is multifaceted. Independent high-resolution datasets are valuable for assessing uncertainties and improving the robustness of meteorological and climatological analyses through multi-dataset intercomparison. In addition, MORE provides a long-term convection-permitting dataset at spatial scales below 2 km, specifically designed for applications over Italy and the Alpine region.
  A further distinctive aspect of MORE is its comprehensive multivariable and three-dimensional structure. Beyond near-surface variables, the dataset includes atmospheric fields such as geopotential height, temperature, humidity, and wind components at multiple pressure levels, enabling physically consistent analyses across different spatial and vertical scales. This characteristic considerably broadens the range of potential applications of the dataset, including process-oriented studies, hydro-meteorological applications, and emerging artificial intelligence methodologies that require long-term, high-resolution, physically consistent atmospheric datasets.
  We agree that these aspects were not sufficiently emphasized in the original manuscript and will revise the Introduction and Conclusions accordingly.
  Comment 2: Verification scope. There is a clear imbalance between the number of variables produced and delivered by MORE and the two variables selected for verification. I acknowledge that soil properties are implicitly evaluated through the application sections. In general, I agree that 2-metre temperature (t2m) and total precipitation (tp) are likely to be the most widely used variables among the target user community, and that they benefit from relatively dense observational networks, making observation-based verification straightforward. However, in its current form, the manuscript does not clearly articulate the rationale behind this choice, nor does it explain why upper-level atmospheric variables are excluded from the verification exercise. A comparison between ERA5 and MORE at upper levels would, for instance, have been an informative addition. I am not asking the authors to perform such additional analysis, but rather to provide a transparent justification, either in the Introduction or in the Methods/Evaluation section, for the decision to focus exclusively on surface variables. Where relevant, the authors could also direct readers to existing literature in which MOLOCH has been evaluated against other variables (e.g. wind, pressure, upper-level temperature), thereby contextualising the verification choices made here and reassuring readers that the model's performance for those variables has been assessed elsewhere.
  Reply2: We thank the reviewer for this thoughtful comment. We agree that the manuscript does not currently provide a sufficiently explicit justification for focusing the evaluation on precipitation and near-surface temperature, despite the much broader range of variables available within MORE.
  The choice was primarily motivated by the fact that precipitation and 2-m temperature are among the most widely used variables for hydro-meteorological, climatological, and impact-oriented applications. In addition, they benefit from dense and long-term observational datasets over Italy, allowing a robust multi-decadal validation framework based on independent observations.
  We also agree that MORE includes many additional variables, including upper-air diagnostics, wind fields, soil variables, and radiation-related quantities, which are potentially of great interest to users. However, a systematic validation of the full set of variables available in MORE would require dedicated analyses that go beyond the scope of the present manuscript, whose primary objective is to introduce the dataset and provide a first comprehensive assessment of its performance.
  Following the reviewer’s suggestion, we will revise the manuscript to better clarify the rationale behind the verification strategy adopted in this work. We will also provide references to previous studies in which the MOLOCH model has been evaluated for additional atmospheric variables and upper-level fields, thereby offering readers further context regarding model performance beyond precipitation and near-surface temperature.
  Comment 3: Communication of evaluation results. I have some uncertainty regarding the overarching motivation behind the evaluation as presented. In places, the text and its interpretations read as a sanity check of the MOLOCH downscaling. This is a legitimate goal, but the suitability of MOLOCH for dynamical downscaling is already well supported by prior literature. I would argue that the most important message of the evaluation is currently underemphasised, and that the text would benefit from revision to remove repetitions (e.g. the concepts in lines 711-714 are repeated several times across the manuscript) and improve readability.
  
  In my view, the central contribution of the evaluation should be to give prospective users a concrete sense of the accuracy and precision of MORE for the verified variables at the temporal aggregations considered. Specifically, I would encourage the authors to address the following questions explicitly: What is the typical uncertainty of daily precipitation estimates from MORE, expressed, for instance, as a relative error? How does this change at hourly aggregation? What uncertainty should a user expect when working with 2-metre temperature? The answers to these questions are arguably the most practically valuable outcome of the evaluation, yet they are not sufficiently prominent in the current manuscript.
  
  Equally important is guidance on the appropriate use of MORE: for which types of analysis is the product recommended? Are there specific regions, seasons, weather regimes, or applications for which MORE is known to perform less reliably, and where its use should therefore be approached with caution? This information, both the quantified uncertainty estimates and the user guidance, should be given a much more prominent role in the paper, and should be reflected clearly in the Conclusions and the Abstract.
  Reply 3: We thank the reviewer for this important and constructive comment. We agree that one of the primary objectives of the evaluation should be to provide prospective users with a clear understanding of the expected accuracy, uncertainty, and limitations of MORE for the variables and temporal scales considered.
  While part of this information is already contained in the current evaluation, we acknowledge that it is not sufficiently emphasized and that some discussions focus excessively on intercomparison aspects. We agree that a more explicit interpretation of the results would substantially improve the practical value of the manuscript for future users of the dataset.
  In the revised manuscript, we will therefore strengthen the discussion of the expected uncertainty ranges associated with precipitation and near-surface temperature, highlighting how these uncertainties vary across temporal scales, seasons, and geographical regions. We will also provide clearer guidance regarding the appropriate use of MORE, including a discussion of the main strengths and limitations emerging from the validation and application analyses.
  More generally, we will revise parts of the evaluation section to reduce repetitions and improve readability, placing greater emphasis on the information that is most relevant for prospective users, namely the expected accuracy of the dataset, the interpretation of its uncertainties, and the contexts in which its use is most appropriate.
  Comment 4: Lines 113-119. The statement on "ensemble approaches" is not clear. Consider moving this paragraph to Conclusions?
  Reply 4: We thank the reviewer for this comment. We agree that the original use of the term “ensemble approaches” was potentially ambiguous and could be interpreted as referring to ensemble forecasting methodologies, which was not our intention.
  Our objective was instead to emphasize the value of MORE as an independent convection-permitting dataset contributing to multi-dataset analyses and intercomparison frameworks over Italy and the Alpine region. We agree that this aspect was not sufficiently clear in the original manuscript and we will revise the corresponding paragraph accordingly, clarifying the role of MORE within the growing set of high-resolution regional reanalyses and hindcasts and avoiding the ambiguous use of the term “ensemble approaches”.
  Comment 5 : Lines 211 and following paragraphs. The manuscript would benefit from a more explicit statement of the objectives underlying the two distinct strands of the evaluation. What insights do the authors expect to gain from comparing MORE against observations? And what is the purpose of comparing MORE against other reanalyses? I believe the answers are implicit in the text, but leaving them for the reader to infer is an unnecessary source of ambiguity. Once again, I would encourage the authors to state these motivations clearly and upfront. For instance, framing the comparison against observations as an assessment of the accuracy and precision of MORE, and framing the comparison against other reanalyses as a sanity check confirming that the downscaling procedure does not introduce gross inconsistencies relative to established products.
  On a related point, there is a degree of internal tension in the manuscript: the authors state that they do not intend to rank the reanalyses or hindcasts (line 308) under comparison, yet the structure and language of the evaluation section largely does exactly that. I would ask the authors to resolve this inconsistency. If the comparison with other reanalyses is intended as a sanity check rather than a performance ranking, then the criteria for a satisfactory outcome should be made explicit. What would a successful sanity check look like? What level of agreement, or disagreement, with other reanalyses would the authors consider acceptable, and on what basis? Providing clear answers to these questions would considerably improve the interpretability of this section for the reader.
  reply 5: We thank the reviewer for this important and insightful comment. We agree that the original manuscript does not sufficiently distinguish the different objectives underlying the comparison against observations and the comparison against other reanalyses and hindcasts, which may lead to ambiguity in the interpretation of the evaluation framework.
  Our intention is indeed twofold. First, the comparison against observations is aimed at assessing the accuracy, uncertainty, and realism of MORE in reproducing precipitation and near-surface temperature variability across different temporal scales and climatic conditions. Second, the comparison against existing reanalyses and hindcasts is intended to place MORE within the context of currently available high-resolution products and to assess whether the long-term convection-permitting downscaling strategy produces physically consistent behaviour relative to established datasets.
  We also agree that the manuscript currently contains some tension between the stated objective of avoiding a formal ranking of products and parts of the discussion that may inadvertently be interpreted in that way. Our intention is not to identify a “best-performing” dataset, but rather to evaluate the consistency of MORE with observations and with the range of behaviours exhibited by existing high-resolution products, while also assessing the capability of kilometre-scale convection-permitting downscaling to realistically represent fine-scale atmospheric variability.
  Following the reviewer’s suggestion, we will revise the evaluation framework to make these objectives more explicit, clarify the interpretation of the intercomparison, and reduce ranking-oriented language throughout the manuscript. We will also better explain what is meant by a satisfactory level of consistency between MORE and existing products, and how this information should be interpreted by prospective users of the dataset.
  Comment 6 : If the comparison with other reanalyses is intended as a sanity check, upscaling MORE to the coarser reference grid would be a more methodologically appropriate basis for the intercomparison. Evaluating global reanalyses on the native high-resolution grid of a convection-permitting product places them in an inherently unfavourable position (lines 398-402). That said, such a comparison may carry practical value by illustrating quantitatively what is lost by forgoing dynamical downscaling, and may discourage the uncritical use of global reanalyses for applications requiring high spatial detail. If this is part of the authors' intent, I would encourage them to state it explicitly.
  Reply 6: We thank the reviewer for this valuable comment. We agree that remapping all products to a common coarse-resolution grid would be an appropriate strategy if the sole objective of the intercomparison were to assess consistency among datasets.
  However, as discussed in our response to Comment 5, the evaluation framework adopted in this study has a broader objective. In addition to assessing the consistency of MORE relative to observations and existing reanalysis products, we also aim to evaluate the dataset at the spatial scale for which it was specifically developed and intended to be used. In this context, part of the motivation is indeed to investigate the extent to which kilometre-scale convection-permitting downscaling contributes to the representation of fine-scale atmospheric variability and spatial structures that may not be fully captured by coarser-resolution products.
  We agree that this rationale was not sufficiently explicit in the original manuscript. Following the reviewer’s suggestion, we will clarify the methodological motivation behind the adopted validation strategy and the interpretation of the intercomparison results, explicitly distinguishing the consistency assessment aspect from the objective of evaluating the potential benefits of convection-permitting downscaling.
  Comment figure 13 : Figure 13. The use of dots renders the figure somewhat difficult to interpret. The authors may wish to consider replacing dots with lines, which would sacrifice some precision in the representation of individual values but would likely improve the readability of the distributional tails considerably
  Reply Figure 13: We thank the reviewer for this suggestion. We agree that readability of the distribution tails is important and we will further explore possible improvements to the graphical representation. While preserving the information conveyed by the current PDF representation, we will investigate whether alternative plotting choices can improve the readability of the figure in the revised manuscript.
  Comment figure 14 : Figure 14. Change MOLOCH-ISAC into MORE.
  Reply figure 14: We thank the reviewer for noticing this inconsistency. The label “MOLOCH-ISAC” in Figure 14 will be replaced with “MORE” in the revised manuscript.
  
  Citation: https://doi.org/10.5194/essd-2025-789-AC1
RC2:
'Comment on essd-2025-789', Anonymous Referee #2, 11 Oct 2026
This manuscript presents MORE, a dynamical downscaling of ERA5 with the non-hydrostatic MOLOCH model at about 1.7 km over Italy and the Alpine region, at hourly resolution from 1990 onward. The authors evaluate precipitation and near-surface temperature against several high-quality gridded observational datasets and compare MORE with other regional and convection-permitting products. They also show three applications: the May 2023 Emilia-Romagna flood, from both a meteorological and a hydrological perspective, and a climatology of tropical nights.
I enjoyed reading this paper which presents a solid and carefully executed piece of work. The validation is thorough and covers several temporal scales. I appreciated the temperature evaluation on the model’s own grid and elevation, which avoids a common source of error. The applications show nicely what the dataset can be used for. Most of my comments ask for clarification, better documentation of the dataset, or corrections of internal inconsistencies. I recommend minor revision.

General Comments
The manuscript currently gives conflicting information about what is released:

The Abstract describe 1990–present, hourly, 40 variables at the surface and on five pressure levels.

Section 5 states that Version 1 on Zenodo contains only hourly near-surface temperature and precipitation for 1991–2020. Additional variables “remain accessible internally” and will be released later under a separate DOI.

Two different DOIs are given: 10.5281/zenodo.18470948 in the text, and 10.82214/EQ2W-2580 in the reference list

Please make the text consistent, stating clearly which variables and which period are openly available now. I also suggest a short “Dataset description” subsection documenting: grid and projection (rotated or regular lat-lon?), grid dimensions and exact domain boundaries, file format and conventions (e.g., NetCDF/CF), file organisation and variable names, units and levels, the planned update policy (“periodically updated”, line 32).
Section 2.1 should better document the configuration: number of vertical levels and model top, main physics options (microphysics, radiation, etc.), time step, whether ERA5 is taken from model levels or pressure levels. Moreover, the nesting goes directly from ~31 km to 1.7 km, a ratio of about 18. A sentence justifying this choice, with supporting references, would help.

The authors acknowledge the difference between dynamical downscaling and data assimilation. Because MORE assimilates no observations, I suggest stating clearly in the Abstract that it is a dynamical downscaling (hindcast) without data assimilation. This is important to know as this means that the large-scale state and its temporal homogeneity are inherited from ERA5, including changes in the ERA5 observing system and the small-scale features are not constrained by observations.

ERA5-Land precipitation is essentially ERA5 precipitation interpolated to a finer grid rather than re-simulated at 9 km. The comparison therefore shows the added value of MORE over its driving model. It would be good to say so.

Specific Comments
Lines 273–274: Contingency tables are computed “at each grid point and over the full study period”. Please explain how they are aggregated into the single point per dataset in Fig. 12? For the annual thresholds (lines 498–500), please clarify what constitutes an “event”.

Lines 284–286: Justify the 0.1 mm threshold given typical gauge resolution which is ususally 0.2 mm.

Lines 423–444: The box plots show the spatial distribution of biases across grid points. Calling this “uncertainty” is confusing while “spatial spread of the bias” would be more precise.

Lines 627–630: The text says MORE has a slightly worse CBIAS than MERIDA-HRES in all seasons except winter. Table 3 shows the opposite.

Table 4: The reported annual values are inconsistent with the seasonal values. For several products the annual bias lies outside the range of the four seasonal values. In general check the text for the reported values.

Line 670: Table 5 is referenced but does not appear in the manuscript.

Lines 876–877: Relative biases in mean, frequency and intensity are reported to “generally fall within −5% to +25%”. However, MORE’s daily intensity bias is −15.6% (SON) and −9.0% (JJA) in Figs. 5–6, and hourly intensity in SON is −10% (Fig. 7). Please check the reported numbers.

Refereence list. Please revise the list as many references are cited but not listed (such as various references to Brocca et al.) and vice versa.
Citation: https://doi.org/10.5194/essd-2025-789-RC2
- AC2: 'Reply on RC2', Paolo Stocchi, 14 Oct 2026
  
  Dear Reviewer,
  We sincerely thank you for your careful and constructive review of our manuscript and for your positive assessment of the work presented.
  We particularly appreciate your recognition of the thoroughness of the evaluation, the methodological choices adopted for near-surface temperature validation, and the relevance of the applications illustrating the potential uses of MORE.
  Your comments provide valuable suggestions for improving the clarity, completeness, and reproducibility of the manuscript. In particular, we acknowledge the need to better document the dataset currently available to users, provide additional technical details on the MOLOCH configuration and experimental setup, and clarify the distinction between dynamical downscaling and conventional reanalysis involving data assimilation.
  We also appreciate your careful examination of the quantitative results and the internal inconsistencies you identified. These points will be thoroughly checked, and the necessary corrections and clarifications will be incorporated into the revised manuscript.
  We will address all your comments in a detailed point-by-point response during the next stage of the review process, together with the submission of the revised manuscript. We believe that your suggestions will help us improve the clarity, transparency, and overall quality of the paper.
  Thank you again for your valuable contribution and for your positive recommendation.
  Best regards,
  Paolo Stocchi, on behalf of all co-authors
  
  Citation: https://doi.org/10.5194/essd-2025-789-AC2

Paolo Stocchi, Francesco Cavalleri, Mohsin Tariq, Michele Brunetti, Stefania Camici, Daniele Mastrangelo, Fabio Di Sante, and Silvio Davolio

Data sets

MOloch-downscaled ERA5 REanalysis (MORE) Paolo Stocchi, Francesco Cavalleri, Mohsin Tariq, Michele Brunetti, Stefania Camici, Daniele Mastrangelo, Fabio Di Sante, and Silvio Davolio https://doi.org/10.5281/zenodo.18470948

Paolo Stocchi, Francesco Cavalleri, Mohsin Tariq, Michele Brunetti, Stefania Camici, Daniele Mastrangelo, Fabio Di Sante, and Silvio Davolio

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Short summary

The MORE (MOLOCH Reanalysis over Italy) dataset provides high-resolution (~1.7 km) hourly weather data for Italy from 1990 to today, including precipitation, near-surface temperature, and 40 atmospheric variables. Validation shows it accurately reproduces local rainfall, temperature patterns, and extreme events, outperforming coarser datasets. MORE enables improved climate studies, flood risk assessment, and serves as a reference for climate services and advanced modeling applications.


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