This manuscript introduces a drought product with a multitype and multiscalar drought index, SZI_snow. The SZI_snow dataset considers a relatively comprehensive extent of the hydrometeorological variables associated with drought development. It uniquely incorporates snow processes in the derivation of a drought index. This consideration is important as global warming has been affecting hydrological processes over the snow-covered regions. The dataset was evaluated across different spatial scales. The result shows the SZI_snow has good performance over the snow-covered regions. The dataset was also used to survey the evolution of large drought events with the severity‒area‒duration method.

The topic of the study is interesting and well fits the scope of the journal, especially for this special issue. The manuscript is well written, logically organized, and the details of the derivation of the SZI_snow are easy to follow. The data processing is careful and well documented, and the dataset was friendly to access. However, there are still some concerns that need to be addressed. Thus, I am supportive of the publication after a minor revision to further improve the quality or make it more clear for the readers to understand the results. Below are my suggestions:

General comments

#1 The main improvement in the SZI_snow is the consideration of snow related processes, thus it is reasonable that the SZI_snow in the snow-covered basins have a better performance than that of SZI as shown in Figure 3. However, I found that both the SZI_snow and SZI have a similar performance over the snow-free basins. Please clarify this similar performance.

#2 The evaluation of the SZI_snow is important compared to current drought indices (for example the scPDSI). Besides the SPI, I think the scPDSI is also a good index to assess the meteorological drought. Thus, it is necessary to evaluate the performance of SZI_snow to capture meteorological drought compared with other indices instead of only with SPI. This can help to confirm the robustness of your conclusion.

Specific comments

#1 Is it possible to include the acronym of your new drought index in the title? Such inclusion can enhance the recognizability of your dataset and facilitate others to cite and employ your dataset.

#2 I suggest adding the spatial resolution of your dataset in the Abstract section.

#3 The author listed various current drought indices in Figure 1. Please make sure the corresponding references of these indices are supplied in the manuscript.

#4 Line 38: Correct the “focus” to “focuses”.

#5 Line 288: Did the two subplots in Figure S3 have identical contour levels and color bar? If did, please remove one color bar.

#6 Line 144: Consider adding a comma after the introductory phrase “meanwhile”.

#7 Line 300: What does the shading in the Figure 4d panel mean? It should be clarified in the Figure caption. The inset of the Figure 4d panel seems not clear to me, you can increase the resolution of your figure.

#8 Line 315: Did the subplots in the left and middle columns of Figure S3 have identical contour levels and color bar? If did, please remove one color bar.

#9 Line 330: Some corner strings (e.g. "a", "b" in panels) are bolded, but some aren’t. Please keep the format of these strings consistent across the manuscript.

#10 I recommend adding the values of trends in Figure 7 so that the reader can know more information from the figure.

#11 Line 352: It is a good way to use a country to describe the area of a drought event. Please add a number to show how large is the size of Guatemala

#12 Line 400: The Interdecadal Pacific Oscillation just appears one time, it is not necessary to provide an abbreviation for it. Please correct the same problems throughout the manuscript.

Dataset

I downloaded the compressed files including all the files from http://doi.org/10.5281/zenodo.5627369. With the software of “ncdump” and “Panoply”, I checked the data files and had no problems to read and visualize the data. All the data is consistent as they were described in the manuscript.

Here I give some recommendations to improve the user-friendliness of the proposed dataset.

#1 The compressed file with a suffix of “.zip”. Is it possible add an introduction about how to unzip this kind of file?

#2 The SZIsnow datasets have different timescales, thus, months before the timescale are set as missing values. It would be nice to give a clear introduction about the missing values in your dataset. 

The authors developed a global dataset of standardized drought indicators incorporating snow information. They first evaluated the indicator and then employed it for the drought temporal-spatial analysis across the globe. This is an interesting dataset for the drought analysis at the global scale. I have several comments as follows. 

Figure 1 In the box of hydrological drought. The advantage (green box) is “considers snowpack and water storage” and the disadvantage (red box) is “unsatisfactory performance over snow covered regions”. What is the reason for the “unsatisfactory performance over snow…” if these indices already consider “snow pack”?

Figure 1 It seems the SZI addresses all the challenges of the indices mentioned in this figure. Is there any remaining disadvantage or limitation of SZI? Please clarify. 

Lines 133: “We evaluated the ability of the SZIsnow and SZI to capture different” It is generally hard for a single drought indicator to capture all types of droughts. Is the SZI designed to capture all drought types？ 

Lines 201-202: Here P and Psnow are used to define the water supply deficit. This equation is only applied in regions and seasons with snowfall, right? What about other regions (e.g., tropics)? Do you use a different set of equations to calculate SZI?  

Lines 244-245: Here the authors used SPI to evaluate the proposed index SZIsnow. SPI mainly reflects precipitation-related droughts. There may not be snow information in SPI (it does not incorporate snowfall, right?). How do we know a higher SZIsnow-SPI correlation reflects better performance of the proposed index? Please justify this. 

Lines 294-295: It seems the proposed index performs better for long-time scales. Any specific reason for this? Please clarify.  

Figure 6. This trend analysis for each grid is performed for the annual time series? Or for a  season? Please clarify.

Figure 7 Any speculation for the low SZI and high dry areas during 1985-1990?  

Tian et al. proposed a drought dataset for indicating drought across multiple categories and temporal scales. The proposed SZI_snow dataset includes different physical water-energy processes, especially snow processes. The evaluation for different spatiotemporal scales indicates the dataset can distinguish different types of drought. The SZI_snow shows superior performance over the cold regions. In addition, the dataset successfully described large-scale drought events over the world. 

The purpose of the proposed work is clear and essential in order to establish a drought dataset including snow information for other studies related to drought. The manuscript is generally well-structured, the method description, evaluation, and data availability were well-written. However, several points should be addressed in the revised manuscript. I offer comments below in the hope this can be used to improve the paper further.

Major comments

First, in lines 102-103, the author stated that the GLDAS-2 drives the Noah land surface model (LSM), forced by the global Princeton meteorological forcing data, to approximate the observed land surface state. However, the Noah is not the only land surface model used by the GLDAS-2. I get the information from the URL: https://ldas.gsfc.nasa.gov/gldas. Could you explain why you chose the Noah model? Are there any differences among these land surface models? Second, the author adopted the log-logistic distribution to standardize precipitation, streamflow, and soil water storage to compute the Standardized Precipitation Index, Standardized Streamflow Index, and Standardized Water Storage Index. As I know, other probability distributions can be used to standardize. Are your evaluation results independent of different methods?

Minor comments

1) Lines 19-20, 22 

Some numbers keep two decimals, and some do not.

2) Lines 99-101

Show some reasons to explain why the better performance of GLDAS-2 compared to that of GLDAS-1?

3) Line 140

I did not quite understand why the WER is regarded as a comprehensive drought indicator? What is a comprehensive drought indicator? Is there any definition for it? Please give more information.

4) Line 155

Did you evaluate the capacity of your dataset across different climate zones? I did not see these mentioned climate zones in the following sections of the manuscript. I guess you wanted to say that the evaluation was conducted over different geographical parts of the world. Please clarify this.

5) Lines 221-222

What is the name of dimensions in your 3D and 2D dataset? Please clarify this.

6) Line 273

It seems this content has been repeated in the main text. Delete it to make the caption more concise.

7) Line 353

Is the geographical extent of Oceania equal to that of Australia? Give an exact definition.

8) Why wasn't Greenland included in Figure 6? It seems all the Greenland are missing values.

9) When you did the SAD analysis, how to process the drought over the Sahara Desert?

Technical comments:

1) Figure 1: The font size of the description to compare the strength and weakness of each index is small.

2) Figure 4: The font should be the same, use one type.

3) Figure 6: The stippling seems unclear.

4) Figure 9: Please supply information on the geographic coordinate system used in this figure. It can help others to compare their results with yours.

5) Figure S2: Adjust the minimum value of the legend. There is no grid with a SZI_snow value less than -4.0.

Comments for the dataset

1) Table 1 in the metadata file should be kept the same as Table 2 in the manuscript.

2) Add information relative to the size of decompressed files in the metadata file.

3) I recommend providing a thumbnail of your dataset in the metadata file.

4) If possible, provide some scripts for potential readers to plot your data.