The authors have developed a high-resolution 3D wind field dataset (YRD1km) over the Yangtze River Delta by running WRF driven by ERA5 reanalysis, assimilating observations, and updating land-use information. This dataset addresses a significant lack of high-resolution, 3D wind products in this important region during the summer months. However, the manuscript still has several structural and methodological issues. Therefore, I recommend that it be considered for publication only after major revision.

Comments:

1. The manuscript evaluates the dataset mainly using MAE / RMSE / NSE, but the verification metrics used in Figures 5 and 6 appear inconsistent with those used elsewhere. Unless there is a justified reason for deviation, please ensure that the verification metrics are consistent throughout the manuscript. If this is not possible, please provide an explanation in the methods section or the figure captions.
2. The text in lines 242–257 appears to be duplicated. Please check the manuscript carefully and remove any redundant content.
3. The manuscript’s validation focuses on 10-m near-surface winds, a single radiosonde station, and an individual case study, which is insufficient to demonstrate the three-dimensional characteristics of the dataset.
4. Please ensure that abbreviations are standardised throughout the manuscript (e.g., “EWC2020” and “ESA2020”).
5. The manuscript states that YRD1km uses 61 vertical model levels, whereas line 400 refers to 32 standard levels. Are the 32 standard levels a subset of the 61 model levels, or are they obtained by interpolation? A clear description of the vertical level and the interpolation method should be provided.
6. Table 1 and Figure 3 only present results for a single day, which may be affected by temporary weather conditions. To obtain more robust conclusions, longer time periods should be included.
7. Please specify the number of training and testing samples in Table 3 or in the methods section, and describe the sampling strategy. The results of independent validation with withheld stations should also be presented.
8. For the Baoshan station used in validation, please provide the station metadata (latitude and longitude, elevation, and underlying surface/land-use).
9. When validating in mountainous and lake regions (e.g., Figure 4), please include terrain/lake contours or DEM information in the maps to make them clearer.
10. Please ensure that the numerical precision and formatting are consistent throughout the manuscript (e.g., 26% in Lines 199 and 46.67% in Table 3).
11. The data download link provided in the abstract points to files that are currently unavailable for download.

The authors have created a high-resolution, dynamically downscaled dataset over the Yangtze River Delta called YRD1km, based on the WRF model, dynamically downscaled from the ERA5 reanalysis. The dataset is then further refined using a hybrid nudging approach, combining observational nudging and analysis nudging, to produce a generally skillful 3D wind dataset. The authors verified the dataset against surface observations primarily for June 2022, as well as against radiosonde observations, and for a convective case study event, also in June 2022. In general, the manuscript is well written and presented, but the analysis, methodology, and conclusions drawn from this work (and particularly the verification) are incomplete. I do believe this work is valid and important, but I recommend major revisions and inclusion of additional detail + verification work prior to publication.

Scientific Comments:

- Though the dataset is valid for the summer months of 2021-2023, most of the verification was conducted in June 2022, with the first few figures and tables focusing solely on June 1, 2022. Additionally, the time period for the daily verification statistics for Table 3 is unclear; also, the time series for Figure 5 wasn't clear (are those averaged MAE/NSE across all stations?). Though I don't doubt that 1-km WRF would generally outperform the 0.25-deg ERA5 for winds, the shown verification statistics should be more transparent and comprehensive; otherwise it feels like "picking and choosing".

- The model setup is not particularly clear, e.g. the justification of the physics parameterizations based on sensitivity testing is dubious. Those schemes are the baseline "default" schemes for the WRF model; though I understand that extensive testing of physics schemes would be unfeasible and beyond the scope of this work, many other dynamical downscaling related articles have discussed this and have either used other schemes and/or have provided justification for why they chose the schemes that they did. Additionally, the nesting and initialization setups are unclear, i.e. was two-way nesting used? Did all three domains start at the same time, meaning that the ERA5 initial conditions would be interpolated directly to the 1-km grid? Is a 1 hour spinup really adequate? Are there discontinuities/"jumps" in the data every 6 hours?

- How much of the skillful verification statistics are due to the WRF model/enhanced spatial resolution, rather than an "edit" of grid point values based on observation nudging? If WRF grid points were nudged on the hour, and verified on the hour (and on the same grid points as the nudging? unclear in the manuscript), then there would be a reduction in error metrics via that "edit".

It would have been nice to see a baseline configuration with WRF being run without any nudging at all, to actually determine how much the nudging improved skill. 

Additionally, it wasn't clear how the obs nudging was fully conducted, i.e. what variables were nudged (was it just wind?). Would the other variables (e.g. 2-m temperature, precipitation) be compromised because of this nudging, i.e. is this dataset only useful for wind, and not for other variables?

- Are there not other studies/datasets with high-resolution downscaling over the YRD, that could be compared against? Purely from an interpolation/scale standpoint, of course the YRD1km dataset would be better able to resolve the winds than ERA5, especially if ERA5 grid points were interpolated to the same observation points for verification (i.e. multiple obs stations within the same ERA5 grid box would have the same ERA5 wind interpolated to them, but would have different WRF winds from different WRF grid points).

This ties into the utility of the dataset and the claims of such utility within the article. Statements like "The scale-dependent improvements emphasize the application value of YRD1km for both short-term weather monitoring and long-term climate analyses in the YRD region" are not backed up within the article. There just isn't enough verification conducted, over long enough time periods + seasons, against other competitive datasets, to make such claims. In particular, YRD1km isn't useful for "short-term weather monitoring" because it is dynamically downscaled from a reanalysis dataset (i.e. not useful as a forecast or nowcast dataset, only hindcasting), nor is it long enough yet for long-term climate analyses. I would recommend the authors be more specific about what the YRD1km can be used for, e.g. as a future training dataset for AI-based downscaling (with a longer dataset), or as input into high-resolution air-quality dispersion modelling or even sub-kilometer scale downscaling (down to LES scales), for specific applications (these were stated in the conclusion, but could be made more explicit and specific in other parts of the article).

Minor Comments:

- Should the citation/URL of the dataset be listed directly in the abstract? (not sure on this, please double check)

- Statements like "high spatiotemporal resolution is fundamental to modern meteorological services, wind energy development, and the

safe operation of low-altitude economy..." should be backed with citations, even if seemingly self-evident

- MODIS2001 ---> did you mean MODIS2010?

- Inconsistencies with abbreviations, e.g. LU-ESA2020 in the text but LUT-ESA2020 in the table

- "Figures 5 presents" --> line 360