The authors used an existing global three-dimensional vector data of buildings (3D-GloBFP) to compile a global 1 km spatially continuous UCP (GloUCP) dataset for application in the WRF model. They found that GloUCP not only surpasses existing datasets in accuracy but also provides superior spatial coverage. This dataset would be very useful for future improvements of WRF-Urban modeling. Before it can be considered for publication, I have a few concerns and suggestions for the authors to consider.

Major comments:

1. My major concern is that there are several recent developments of global urban parameter datasets (e.g., UT-GLOBUS data (Kamath et al., 2024: https://doi.org/10.1038/s41597-024-03719-w); U-SURF data (Cheng et al., 2024: https://doi.org/10.5194/essd-2024-416)), which however are totally missing in the introduction and discussion parts of this study. More importantly, what are the novelty, advantages, and uniqueness of GloUCP compared with those recent urban parameter datasets? This needs to be clarified. It would also be useful to conduct comparisons between the GloUCP parameters with those other recent global datasets.
2. Does the GloUCP data also include urban fraction parameter that is consistent with the UCP data? If not, I would suggest including the urban fraction data which is very important to be consistent with the derivation of UCP. The authors mentioned they used GAIA data as a mask. Does this mean users can use the urban/impervious fraction data together with GloUCP data? If so, this needs to be clarified.

Minor comments:

1. Line 65: The reference (Demuzere et al., 2023) for LCZ implementation in WRF is missing. Reference: Demuzere, M., C. He, A. Martilli, and A. Zonato (2023): Technical documentation for the hybrid 100-m global land cover dataset with Local Climate Zones for WRF (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.7670792
2. What year is the GloUCP data representative of?
3. Lines 120-125: It needs to be made clear that these required UCPs are for WRF single layer UCM or all WRF UCMs. WRF includes three types of UCMs (SLUCM, BEP, BEP-BEM). If it is only for single layer UCM, then this should also be made clear in the title.
4. Line 125: What year is the GAIA data used in this study representative of? This info is important for regions with fast urbanization rate.
5. Does the 3D-GloBFP data also include the Ai, Ap, Ar, Aw, Aproj and Ndis parameters mentioned in Table 1? It is not very clear based on the current descriptions.
6. I suggest adding a table to summarize the evaluation metrics/statistics for each dataset for Section 3.2.
7. Figure 6c,f,i: Should the blue bar be Sun2021 instead of NUDAPT in the legend?
8. One thing that is worth mentioning is that the NUDAPT data in WRF is an old dataset developed around 2010, which contributes to the large uncertainties of this dataset.
9. I would suggest adding a small section to discuss the potential uncertainties associated with this GloUCP dataset and the cautions/suggestions for users in terms of correctly using this dataset.
10. Is there a plan for the authors to implement this dataset to WRF Pre-processing System (WPS)?

This article developed the essential data needed to make urban climate simulations based on WRF-Urban as accurate as possible for any city in the world. As the author points out, building morphology data significantly impact the accuracy of urban climate simulations, but such data have only been available for a limited number of cities. This research, which aims to develop a global dataset, is significant in that it attempts to overcome this situation. There is no doubt that the dataset in this paper will be helpful for many WRF-Urban users.

On the other hand, I am concerned that the paper does not refer to previous research that has undertaken similar initiatives. As a result, from the perspective of an individual data user, it is unclear how these data differ from existing global data and what their characteristics and novelties are. There is a need to compare these data with existing global data and describe the characteristics of this dataset before publication is considered.

Major comment:

As mentioned above, the characteristics of the global dataset proposed in this study must be described in relation to similar existing global datasets.

Specifically, we know that a global dataset has been developed and published by Knanh et al. (2023), and these data have been adopted in WRF v4.6.0.

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Paper

https://www.cell.com/heliyon/fulltext/S2405-8440(23)02718-4

Dataset

https://figshare.com/articles/dataset/Present_and_future_1_km_resolution_global_population_density_and_urban_morphological_parameters/17108981?file=31635521 (10 Dec 2024, last access)

WRF Github

https://github.com/wrf-model/WRF/releases/tag/v4.6.0 (10 Dec 2024, last access)

https://github.com/wrf-model/WRF/commit/3cadf04277ac3a050e65461efb6aa939349c60a8 (10 Dec 2024, last access)

https://github.com/wrf-model/WRF/pull/1986 (10 Dec 2024, last access)

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Minor comment:

1. I think the authors developed data for both the single-layer urban canopy model (SLUCM) and the Multi-Layer UCM (BEP). If my understanding is correct, it would be better to describe this kind of information; otherwise, readers might misunderstand. If I understand correctly, a global dataset of the vertical distribution of building heights (for BEP and BEP+BEM) would be a novelty of this work. 

2. It would be helpful for users if you added a table showing the relationship between the parameters in the dataset you developed and the parameters in URBPRAM.TBL in WRF-Urban.

3. I wonder about the correspondence between the parameters of the dataset developed this time and the categories of the global map of Global LCZ. Global LCZ is included in WRF and it is necessary to set geometric parameters etc. for each LCZ category in URBPRAM.TBL. I am therefore concerned about the above correspondence. Is the proposed dataset intended to set values for each WRF grid? Or is it intended to set values according to LCZ? It would be helpful to users if you could describe the author's thinking and recommended setting methods.