This work compares the difference among some soil moisture products in representing the soil moisture drought, and discusses the potential factors that cause this difference. Although the research objective sounds important, the current manuscript is not suggested for publication. The knowledge gap and innovation is not clarified, the implication and suitability of the conclusion is unclear, and the interpretation is confuse and should be revisted carefully. Detailed comments are below:

1. The innovation. The introduction states the importance of the drought and then states that “involved products show partly considerable differences in the global patterns and magnitudes of the soil moisture drying.”. However, either a comprehensive review on the literature that evaluates the ability of different products in capturing drought, or the current knowledge gap on understanding the differences between different products, is provided. This makes it confuse to the reader on the innovation of the current work.
2. The implication and suitability of the conclusion. The current result is based on the intercomparison between different datasets based on a few drought cases (e.g., 19), so the results only indicate the difference between the chosen products (e.g., ESA-CCI, ERA5, ERA5_Land and MERRA2). Then, what is the implication of the results? Which dataset should we relief on? Or which dataset is more suitable to perform drought analysis? In addition, the drought cases are mainly over the Europe and are not enough for a global perspective.
3. The dry-season SM. The dry-season SM in current research is discontinuous, and is different from the usually used concept that is based on a consecutive period with lower SM. Therefore, the meaning of the the linear trend of dry-season SM should be clarified more clearly. In addition, the trend of dry-season SM is used to interpret the difference among different products in representing drought characteristics. This is very confuse to me, because lots of the drought cases happened during the wet seasons (e.g., June-September).
4. The different spatial resolution of products. Was the analysis based on the original spatial resolution of different datasets or a fixed resolution (e.g., aggravate them to 0.25°)? Different spatial resolution would lead to different grid samples in the same drought area, and may influence the result. In addition, the high-resolution products tend to be more heterogeneous and potentially influence the identification of the core zones of drought events.
5. It seems that, the soil moisture in reanalysis products includes both liquid and solid soil water while the remote sensing products only provide the liquid soil water. I suggest the author to confirm this and pay attention to the frozen period when comparing different products.
6. The discussion said that satellite datasets do not consider the dynamic land-surface characteristics and bioclimati and attributes the differences between satellitedataset and reanalyses dataset to the considering of the underlying trends of relevant land-surface characteristics and bioclimatic indicators. However, similar with the satellite dataset, the reanalysis dataset also does not consider these dynamic factors. Therefore, the discussion may be incorrect.

This study investigates the ability of surface and root-zone soil moisture from multiple reanalysis and remote-sensing products in representing drought events in recent 20 years globally, and compares their differences in describing various drought metrics. Overall, this paper provides a comprehensive reference for selecting datasets for drought study. But the structure and conclusions of this article are not clear enough for including too many datasets and drought events, so I suggest a major revision before publication. The main suggestions are as follows.

General comments:

The authors should be more familiar to Europe, and nearly half of the 18 selected events occurred over Europe. So why not just focus on the ability of multiple datasets in characterising seasonal drought events Europe? In Figures.6－7 and 10, the drought metrics show remarkably discrepancies between seasonal and multi-year events. Thus I suggest the reconsideration of the clarification.

Specific comments:

1. The description of data and methods (section 2 and 3) are too long. Although the detailed information may be helpful to readers, it is not suitable in a scientific paper.
2. The figures and tables are not well organized in the paper structure. The quantitative results in tables can be integrated to the respective figures, which can make it more clear and comparableto readers. For example, the area mean of severity, magnitude and duration in Table.2 can be added to Figu1－3, and the maximum of spatial extent of the events to Figure.5. In addition, Figure.4－5 can also be integrated in a Figure as (a) and (b), respectively.
3. In term of the evaluation for the selected drought events, more statistical metrics can be included, such as pattern correlation, RMSE, and so on. Figures.6－9 are displayed only in bars, which is not concise and explicit enough. I recommend the Table graphic type to present each evaluation result for all events and all datasets. The detailed procedure can be seen at https://www.ncl.ucar.edu/Applications/table.shtml.
4. The analysis of dry-season soil moistureis less related with the research objective. I think it is more reasonable to further compare the soil moisture during drought events after presenting the results for multiple drought events.
5. As for the long-term trend, the analysis may be better to be conducted for the drought events rather than another indicator.

The discussion section is not convincing and substantial. In 5.1, For drought metrics and dry-season SM trend were derived from the same variable, they must be related. In 5.2, the attribution method is too simple and no quantitative results are shown.

The study investigates the ability of active and passive based remote sensing soil moisture products and land reanalyses to capture documented drought events and drought trends during the period 2000-2020. The drought events are characterised in different parts of the world by their severity, duration and spatial extent. The events are placed in the context of dry season soil moisture trends and potential reasons for diverging soil moisture trends between the different products are investigated. It is found that all the products capture the selected drought events. Significant differences between the products are found – for example, responses in surface soil moisture tend to be weakest for the active remote sensing products. For the global reanalyses, ERA5 and ERA5-land have a greater tendency for drying trends, whilst MERRA-2 has a greater tendency for wetting trends. Based on other reanalysis variables (evapotranspiration, runoff, precipitation) and observational data, it would appear that the ERA5 and ERA5-land trends are more reliable overall.

The authors have done a detailed and robust evaluation of the different products and have done well to disentangle the reasons (or potential reasons) for the divergences in the results. However, I think the introduction and discussion sections need to be more concise, with some of the detail removed. Further, I think the paper could be strengthened by linking the results to studies where reference soil moisture datasets (e.g. in situ data) have been used to validate drought events and trends (e.g. Li et al., 2020). This would give more weight to the conclusions of the study. Furthermore, I think the rationale for the approach used in this study needs to be more clearly communicated in the abstract and conclusion. Please also see the minor comments below.

Line 46: Replace “trigger” with “triggers”

Lines 76-94: I agree with the rationale for the evaluation approach. However, I still think the authors should link the results to studies where reference datasets have been used in the discussion section (5), as it would reinforce the findings in this study.

Line 148: Suggest to replace “as for” with “to”

Section 2.1.2 ERA5

It is important to mention here that in ERA5, T2m/RH2m pseudo observations are assimilated in the soil moisture analysis (see for example de Rosnay et al., 2013). These observations tend to have an important impact on root-zone soil moisture and latent/sensible heat fluxes with the atmosphere (see e.g. Fairbairn et al., 2019). The sensitivity of ERA5 to drought events could potentially be increased by assimilating these observations.

Line 381: Suggest to replace “the average” with “average”

Line 399: Suggest to replace “of” with “for”

Line 401-403: Please rephrase for clarity and maybe split into two sentences.

Line 497: Suggest to replace “and display” with “display”

Line 526: Sentence starting with “Despite the considerable spread…” Please rephrase as sentence does not make sense.

Line 532: Suggest to replace “largest deviations” with “the largest deviations”

Line 564: Sentence starting with “These regional differences…”. Please rephrase for clarity.

Line 570: Suggest to replace “of MERRA-2” with “for MERRA-2” and replace “of ERA5” with “for ERA5”.

De Rosnay, P., Drusch, M., Vasiljevic, D., Balsamo, G., Albergel, C. and Isaksen, L., 2013. A simplified extended Kalman filter for the global operational soil moisture analysis at ECMWF. Quarterly Journal of the Royal Meteorological Society, 139(674), pp.1199-1213.

Fairbairn, D., de Rosnay, P. and Browne, P.A., 2019. The new stand-alone surface analysis at ECMWF: Implications for land–atmosphere DA coupling. Journal of Hydrometeorology, 20(10), pp.2023-2042.