The paper presents a modelling application which, although not new in terms of methodology, is very well organised and written, follows a proper and logical flow of practical steps, is exhaustively commented in the framework of current specific literature and provides a comprehensive analysis of the state of drought hazard in Sweden. In my opinion the manuscript should be accepted as is.

Using hydrologic data from simulations (S-HYPE model; L96), the manuscript provides detailed insights into trends in Sweden's water budget components—precipitation, evapotranspiration, and surface and soil storages—at a spatial resolution of approximately 40,000 subcatchments. A suite of standardized drought indicators is employed, including the Standardized Precipitation Index (SPI), Standardized Precipitation–Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI), and Standardized Streamflow Index (SSI).

Drought events of varying severities (e.g., extreme, severe, moderate) are identified based on when these indicators fall below predefined critical thresholds (Table 1). Due to the distinct temporal variability of each indicator, the identified drought periods differ accordingly (L217, L429). In addition to SPI, SPEI, SSMI, and SSI, droughts are also detected based on Soil Moisture Anomaly (SMA) values, using the same critical thresholds (Table 1; Figure 2).

Monthly SMA values are used to perform a cluster analysis, resulting in the identification of five regions exhibiting distinct monthly SMA patterns (Figure 1). Overall, the detected trends in drought indices (Figures 4, 5), drought periods, and regional patterns align with prior literature on droughts in Sweden (L207; L288; L330).

I find the study insightful, particularly the identification of regions with homogeneous monthly SMA variations and the trend analysis of standardized drought indicators. However, I have some concerns regarding the methodology used to identify droughts based on each standardized drought indicator independently. While fluctuations in these indicators reflect seasonal transitions from wetter to drier conditions, they do not, in principle, constitute sufficient evidence for the occurrence of drought (see comments #1, #2, and #3).

For a more rigorous assessment, I recommend exploring ways to integrate information from multiple drought indicators to identify drought events, using documented historical droughts as a reference. Alternatively, the authors could consider shifting the study’s focus slightly—from identifying droughts to analyzing trends in established drought indicators and the occurrence of periods with low values, without labeling them as “droughts.” This could be complemented by qualitatively noting the observed agreement between such low-indicator periods and historic droughts, as already discussed in the current manuscript. The latter approach would primarily require a revision in terminology.

A more detailed list of comments is provided below.

Major comments:

1. The authors identify drought events based on the occurrence of negative Soil Moisture Anomaly (SMA) values (L200-209; Fig. 2), but this approach may not be rigorous in principle. According to their own definition (L25), a drought is a period in which water deficits have significant negative impacts on society and ecosystems. However, given the way SMA is defined (Eq. 1; L115-124), it primarily captures monthly fluctuations of average soil moisture around the annual mean value. Seasonal variability naturally leads to SMA fluctuations, but minima in the time series do not necessarily indicate drought conditions. For instance, consider a hypothetical region unaffected by droughts but characterized by strong seasonal climate variations: SMA would still show fluctuations, yet its minima would reflect merely dryer-than-average months—not actual droughts. Within the framework of the current analysis, the regional classification based on distinct patterns of monthly SMA (Fig.1) is compelling. However, the authors should clarify that using SMA alone to identify droughts is not yet a standardized approach and requires further validation. Acknowledging this is particularly important given the ongoing discourse surrounding drought definitions in the scientific community. Practical suggestions for addressing this are outlined in comment #2.
2. As discussed above, a period characterized by markedly negative SMA values may not, in itself, constitute a drought event. Consequently, using SMA as a standalone criterion for drought identification should be supported by a validation procedure that compares low-SMA periods with documented historical droughts. The authors briefly address this comparison (L207; Fig. 2) by applying SMA thresholds of -1.5 and -2 to define “severe” and “extreme” droughts, respectively (Fig. 2 caption). Most of the identified “severe” and “extreme” events—highlighted with black rectangles—correspond to historic droughts (L207–208), with exceptions such as the 2002 and 2006 events. I recommend either expanding this analysis—for example, by providing more information on historical droughts and assessing correlations between drought impacts and the severity of soil moisture anomalies—or revising the terminology in the manuscript. In the latter case, the authors could note that “low-SMA periods below -2 show good agreement with historic drought occurrences, suggesting that monthly SMA may have potential for drought detection; however, rigorous validation of this approach lies beyond the scope of the present study.”
3. L126, L216: Similar concerns raised in comments #1 and #2 apply to the use of some of the standardized drought indicators considered. Among all indicators, SPI and SPEI are widely recognized in the literature, while SSI and SSMI offer a direct representation of drought impacts through its reflection of surface and soil water availability, respectively. My primary criticism lies in the methodological approach: the authors identify drought events independently from each indicator’s time series, assigning drought periods based on when each individual index drops below a predefined threshold (Tab. 1; L217, L224, L429). However, aside from SPI and SPEI, these indicators reflect symptoms—not causes—of drought. Their fluctuations alone do not necessarily imply the presence of a drought, as illustrated in the hypothetical case-study region described in comment #1. Rather than treating indicators independently, I recommend conducting an a-posteriori analysis of documented historical droughts to investigate how patterns across indicators align during known events. Can the interplay between SPI, SPEI, SSMI, and SSI be used synergistically to more robustly identify droughts? The observed discrepancies in drought periods across indicators (L217, L429) suggest that, when considered alone, none provides sufficient information for unambiguous drought classification. While the manuscript appears to touch on this integrative analysis (L228–233; L431), its treatment is preliminary and partly relegated to the supplementary materials. I strongly encourage the authors to elevate this discussion to the main text, as it could form the basis of a substantial contribution to the field of drought monitoring. Alternatively, if such integration is beyond the intended scope, the authors should avoid referring to low-indicator-value periods as "droughts" and revise the manuscript accordingly to reflect this distinction.
4. L158, L159, L160: The physical interpretation of the “accumulated drought intensity” and “accumulated weighted drought severity” metrics remains unclear. Could the authors clarify what these metrics represent, conceptually and physically? Moreover, as “accumulated drought intensity,” “accumulated drought severity,” and “accumulated weighted drought severity” are inherently dependent on the length of the record period, they are expected to increase with longer record periods. This raises the question: why not normalize these metrics, for example, by expressing them as annual means? Doing so may facilitate more meaningful interannual or interregional comparisons. Additionally, it appears that these metrics are introduced but not subsequently used or discussed in later sections of the manuscript. If they do not contribute to the core analysis, their inclusion should be reconsidered or more clearly justified.
5. Sub-section 4.2 of the Discussion offers a broad overview of current limitations in drought characterization and emphasizes the need for more advanced methodologies capable of capturing the diversity of drought impacts across spatial and temporal scales. While this is an important topic, such content may not be entirely appropriate for the Discussion section, which is typically reserved for interpreting and contextualizing the study’s own findings. In this case, the inclusion of this subsection appears somewhat out of place, as the manuscript does not specifically explore or propose novel approaches for drought characterization. If the authors wish to retain this perspective, it might be better positioned in the Introduction—provided they clearly frame it as background context for the current study. Otherwise, this section could be considerably shortened or omitted for clarity and focus.
6. L96-112: It is unclear whether the authors conducted the S-HYPE simulations themselves or relied on results from previous studies. If the simulations were performed by the authors, they should provide all necessary details to ensure reproducibility, ideally supported by access to the relevant datasets and code. Alternatively, if the simulations were sourced from prior studies, the authors should clearly cite these sources and ensure they are appropriately referenced in the Data and Code Availability Statement. Clarifying this point is essential for transparency and reproducibility.
7. Fig. 3: The authors should clarify how drought severity was calculated. Is it defined as a cumulative value over the considered period for each standardized drought indicator and timescale?

Minor comments:

Fig. 3: for the first “severity” category, express the range as "<-30" instead of "from -infinite to -30".

L126-128: Include the equations for SPI, SPEI, SSMI, and SSI in the main text.

L146, L152: I think that “drought characteristics” would be better than “drought parameters”, when referring to drought duration, severity, intensity, and frequency.

L443: As the study does not deal with drought forecasting, I disagree that its findings can enhance early warning systems.  

The Acknowledgments and Data and Code Availability Statement are missing.