the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jun 2026
The Standardized Soil Moisture-Temperature Compound Index (SSTCI): Daily scale global data set and event catalogue for compound dry–hot extremes (1961–2023)
Abstract. Compound dry–hot extremes (CDHEs) are among the most damaging climate hazards. Most metrics used to diagnose them are estimated at monthly or seasonal resolution or rely on precipitation-based drought proxies that can miss fast soil moisture-driven land–atmosphere feedbacks. This paper delivers two tightly linked products for global CDHE monitoring over land (excluding Antarctica) at 0.1° resolution for 1961–2023: (i) a continuous, daily severity index, i.e. the Standardized Soil Moisture–Temperature Compound Index (SSTCI), and (ii) a companion, event-based CDHE catalogue derived from SSTCI.
SSTCI is constructed by first quantifying grid specific soil moisture memory using dry-down events, fitting an exponential decay to estimate a characteristic dry-down timescale (τ). This memory is used to compute a Standardized Antecedent Soil Moisture Index (SASMI), which is paired with a daily Standardized Temperature Index (STI). SASMI and STI are then integrated using a Frank copula to represent their joint probability, which is transformed to a standardized normal variate to yield SSTCI, providing a spatially comparable daily measure of compound dry–hot stress.
To move from daily index values to physically interpretable hazards, we apply removal–merging refinement to threshold based SSTCI spells, minimizing fragmented detection and producing a coherent event catalogue with event timing and magnitude descriptors suitable for event-based statistics. Our evaluation shows broad agreement between SASMI (SSTCI) and established drought (compound) metrics and vegetation stress. Moreover, SSTCI captures the timing and evolution of well documented CDHEs across multiple regions and timescales when applied to case studies. Together, the resulting SSTCI fields and event catalogue provide a consistent, daily scale foundation for global monitoring, process attribution of land–atmosphere feedback, and early-warning applications for compound dry–hot hazards. Data introduced in this paper are openly accessible from https://doi.org/10.5281/zenodo.18280747 (SSTCI) (Aftab et al., 2026c) and https://doi.org/10.5281/zenodo.20826759 (CDHE event catalogue) (Aftab et al., 2026b).
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Status: final response (author comments only)
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RC1: 'Comment on essd-2026-313', Anonymous Referee #1, 13 Jul 2026
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AC1: 'Reply on RC1', Daniel Hagan, 17 Jul 2026
We thank the reviewer for his/her comments. The comments have helped us to state the contribution of the study/dataset more precisely, and to strengthen the physical and methodological motivation for several choices that were presented too briefly in the submitted manuscript. Below, we address each comment raised by the reviewer.
On the whole, we would like to note that ESSD's uniqueness criterion states that "any data set on a variable supposed or suspected to reflect changes in the Earth system deserves to be considered unique," and that its test for methodology is whether "a new or improved method should not be trivial or obvious." Compound dry–hot extremes are a variable of precisely the first kind. On the second, we hope the responses below make clear that the methodological steps involved are neither trivial nor obvious: two components of the index are fitted locally at every land pixel rather than assumed globally, the soil-moisture memory timescale τ, which drives the antecedent weighting in SASMI (Comment 3), and the copula representing the joint moisture–heat dependence, and the event catalogue is constructed through cell-by-cell threshold calibration with distributional testing rather than by thresholding the index (Comment 5).
Detailed responses are hereafter provided:
Comment 1
In the review criteria, ESSD mentions significance as the most important aspect. With regard to uniqueness, it is stated that "it should not be possible to replicate the experiment or observation on a routine basis". The current manuscript, in my view, fails to clear this hurdle representing a more-or-less straightforward application of a standardized index to openly available reanalysis data, with poorly motivated choices that strongly impact the applicability of the dataset (see also below).
We thank the referee for engaging directly with the significance criterion. We are not aware of any openly available dataset that offers a daily, globally complete, soil moisture centric compound dry–hot index over six decades together with a statistically filtered, event-resolved catalogue. The open compound indices we know of are regional, cover shorter periods, are monthly or seasonal, or rest on precipitation-based drought proxies rather than soil moisture. It is this combination that is missing, and that is the gap we aim to fill. The two components also serve different users: the continuous SSTCI field is suited to process-level and attribution research, where a daily signal on a common grid is what such analyses require, while the event catalogue offers a discrete, comparable inventory that impact, risk and exposure studies can work with directly.
Furthermore, the product is not the output of a routine index calculation. At each step, we parameterize locally at every land pixel rather than making global assumptions. Chiefly, the soil-moisture memory timescale τ, which drives the antecedent weighting in SASMI, and the copula linking the moisture and temperature components, which represents their joint dependence and the extreme event catalogue. Our companion paper (Aftab et al., 2026a), which focused on the methodology’s development, demonstrated the SSTCI methodology at a single basin using one uniform τ; a reasonable simplification for one homogeneous region, but not one that transfers to the globe. We set out the physical basis for both choices in our responses to Comments 2 and 3. The catalogue is likewise not a thresholding of the index; the event-extraction procedure and its statistical basis are described in our response to Comment 5.
Each of these steps carries its own computational cost, and they compound. That is, τ is fitted at every land pixel, a copula is fitted at every pixel, the index is evaluated daily across 63 years, and the event identification and its distributional tests are run cell by cell at 0.1° over the global land surface. Reproducing the product is not a matter of running a script over ERA5-Land. We would also note that ESSD has recently published Dheed (Weynants et al., 2025), a global catalogue of dry and hot extreme events built entirely from ERA5, which suggests the significance criterion turns on what a product delivers to users rather than on whether its inputs are public. We release our code alongside the data, and sparing users the burden described above is itself part of what the dataset is for. In the revision we will state this combined contribution.
Comment 2
In their approach, the authors use soil moisture averaged over the top 7 cm of the soil profile. This seems rather arbitrary, as there is no physical reason why the top 7 cm soil moisture would influence air temperatures more than the top 2 cm or 10 cm, or even deeper. In many more humid climates, soil moisture averaged over the root zone would be the best representation of soil moisture impacts on T through land-atmosphere interaction... The authors do not provide a physical justification of this arbitrary choice, nor do they provide a sensitivity analysis.
We thank the referee for this important point and welcome the opportunity to clarify the reasoning behind the choice, which follows from the objective of the index rather than being arbitrary.
SSTCI is designed to resolve the daily-scale onset and evolution of compound dry–hot conditions and the rapid land–atmosphere coupling that drives them. For that target, the near-surface layer is where the coupling is most directly expressed. That is, it controls direct soil evaporation and is most tightly tied to the daytime surface energy balance that governs latent–sensible partitioning, which is the mechanism by which moisture deficits amplify near-surface heat. This is consistent with our use of daytime (06:00–18:00 local time) fields, chosen to capture the window in which that coupling is strongest. The 0–7 cm layer (swvl1) is also the standard and most extensively validated near-surface soil-moisture layer of ERA5-Land, which supports comparability with the broader literature. We would add that the choice of a shallow layer does not leave the index blind to longer-timescale moisture conditions. This is what the SASMI component supplies, where the antecedent weighting with a grid-specific memory timescale carries the accumulated moisture history into the index, while the state variable itself retains the fast response needed to resolve sub-weekly transitions. The formulation is an attempt to obtain memory and responsiveness together rather than to trade one for the other, and the depth choice should be read alongside it rather than in isolation.
We nonetheless take the reviewer’s point seriously, and our formulation does not claim to capture deep vegetation water stress, which is optimized for rapid surface coupling and near-surface conditions. This is already stated in Sect. 3.9, where we note that reliance on top-layer (0–7 cm) soil moisture may not fully capture deeper root-zone constraints that govern vegetation stress and drought persistence, and where we identify root-zone incorporation as a priority for future development. We will make this trade-off more prominent in the revision and would note that the framework is fully extensible in this direction. ERA5-Land provides soil moisture at multiple depths on the same grid, so a root-zone or multi-layer variant of SSTCI can be constructed directly within a similar framework, as the authors believe (at least from preliminary analyses) that sub-surface development would require critical parameterization of the framework. Additionally, a systematic comparison across depths is a natural next step that the present, near-surface product is designed to enable. We also note that it was important for the authors to first receive community feedback based on the current form, based on which we would further develop the sub-surface products, which is currently being developed. Here, we are following the principled used by other major groups such as the H-CEL’s GLEAM which started from the surface to the root-zone, combining lessons from the community’s feedback.
Comment 3
The authors calculate the soil moisture part of their index based on SASMI which is using an e-folding timescale. The added value of this approach is not made clear. What is the benefit of using this method over using any other possibility, including using SSMI calculated using soil moisture from ERA5-Land directly? The choice again feels arbitrary, and in my opinion results in less realistic values than alternative choices.
We thank the reviewer for raising this, as it lets us clarify the physical motivation for SASMI, which we agree was stated too briefly in the original manuscript.
The added value of SASMI over an instantaneous standardized soil-moisture index is physical rather than procedural. A plain SSMI characterizes the soil-moisture state on a single day, which could be considered as a snapshot of anomaly. Compound dry–hot coupling, however, is not driven by the moisture state on any one day but by the accumulated antecedent depletion over the preceding period. Thus, it is the progressive drydown of moisture that suppresses evaporative cooling and shifts surface energy partitioning toward sensible heat, preconditioning the surface for heat build-up. Antecedent memory is therefore almost an inevitable refinement of the signal, since it is the mechanism through which soil moisture modulates temperature, while a memoryless snapshot omits precisely the quantity that governs the coupling. The 2010 Russian mega-event (Fig. 5) illustrates this directly. In that event, a sustained early Summer soil-moisture decline precedes and physically enables the subsequent heat extreme, which is a sequence that an instantaneous index would easily miss.
SASMI encodes this mechanism by weighting antecedent soil-moisture states with an exponential decay whose timescale is the grid-specific dry-down constant τ, estimated from each cell's own observed dry-down behavior. This is a deliberate improvement over both a raw daily snapshot and a uniform, globally fixed accumulation window. Fig. 1 in the manuscript shows why the latter is not defensible at global scale, where τ falls below 5 days across arid and semi-arid regions and reaches or exceeds 20 days in the Amazon, Central Africa and maritime Southeast Asia, with temperate and managed regions intermediate at roughly 7–15 days, a pattern consistent with the aridity and land-surface controls documented by McColl et al. (2017). A fast-draining semi-arid cell and a slowly buffered humid cell do not share a memory timescale, and thus, SASMI adapts the weighting accordingly rather than imposing one assumption everywhere. The authors believe that this choice is grounded in the observed physics of soil-moisture memory, which we characterize globally in Sect. 3.1, rather than being arbitrary.
On the concern that SASMI may yield less realistic values, our evaluation does not support that reading. SASMI agrees well with two independent, established daily drought metrics, SPEI and DEDI, across most global land areas (Fig. 2). We take this as evidence that the antecedent weighting preserves drought-signal fidelity, it recovers the drought behavior those metrics identify rather than distorting it, while operating directly on soil moisture at the grid cell's own memory timescale, which is what the compound coupling requires and what neither metric provides.
In the revised manuscript we will make this rationale explicit in Sect. 2.3: the distinction between an instantaneous state and antecedent memory, and the role of the grid-specific τ in setting it.
Comment 4
The authors claim that "Yet, most existing compound indices are provided at monthly or seasonal scales". This does not really do justice to the large body of literature on multivariate indices for heat-drought extremes. I refer here to Miralles et al, 2012 and Shan et al. 2024 as examples... The novelty of the study should be motivated better based on a more thorough literature review.
We thank the referee for this constructive pointe. We agree that the sentence in question was framed too broadly and did not adequately reflect the substantial literature on multivariate and compound heat–drought characterization We will revise the Introduction to give a fuller and more balanced account and to motivate our contribution against it. Nonetheless, the authors would like to clarify a couple of points about the references provided.
Miralles et al. (2012) worked on a foundational contribution to understanding soil-moisture–temperature coupling and the role of soil desiccation and atmospheric heat accumulation in mega-heatwaves. Their study thus directly reinforces the physical rationale for a soil-moisture-centric compound index, which is central to our approach, and we already draw on the closely related Miralles et al. (2014) for the land–atmosphere feedback perspective. We would note, though, that this line of work characterizes the coupling mechanism rather than delivering a multivariate compound index or an openly available compound dataset. More importantly, the goal of Miralles et al (2012, 2014) was to understand the role of soil moisture–temperature feedback during heatwaves, and not directly extendable to droughts. This is because the formalism of Pi requires a strong positive correlation of soil moisture deficit with air temperature to be significant. As a result Pi is not optimum in some cases for studying the full evolution of the compound event (personal correspondence with Diego Miralles). Thus, their study motivates our product rather than overlapping with it, and we will use it in that role in the revised Introduction.
Shan et al. (2024) is likewise relevant and closely related to our work, we adopt its removal–merging event identification procedure. It does not, however, provide a compound index in the integrated sense that SSTCI does. There, drought and heat are represented as two separate standardized series, a precipitation-based daily (SPI) and a standardized heat index (SHI), and compound events are defined through their temporal set relationships rather than by combining the drivers into a single quantity. SSTCI instead integrates the soil-moisture-based drought signal (SASMI) and the temperature signal into one continuous standardized compound variate through copula, so that the joint dry–hot dependence is carried within the index itself. The two approaches are complementary rather than equivalent.
Taken together, these references let us position the contribution more precisely, and we will expand the Introduction accordingly, acknowledging this broader literature while clarifying that, to our knowledge, no existing openly available product combines a daily, global, multi-decadal, soil-moisture-based compound index with an objectively constructed event catalogue.
Comment 5
The authors claim that a main novel aspect in their work is the catalogue of event timing and magnitude per location. I do not consider this particularly novel, since it is rather straightforward to do once the index has been defined and calculated. A more novel aspect would be to define unique spatio-temporal clusters of heat-drought events, and catalogue these rather than the just the temporal dynamics for each grid cell.
We thank the reviewer for this thoughtful point. On the catalogue itself, we would like to note that its contribution lies less in conceptual simplicity than in what its construction requires and what it delivers.
The catalogue is not a thresholding of the index. Events are identified through an objective removal–merging procedure (Fig.S1 and refer to Aftab et al., 2026a, figure 4 for better understanding) in which minor spells are removed and mutually dependent spells merged, with thresholds chosen so that the retained events are statistically independent (exponentially distributed inter-arrival times) and belong to a genuinely extreme population (GEV-distributed severities). Critically, these are not global settings: the thresholds are searched and the distributional tests evaluated cell by cell, so that every land pixel receives its own calibrated pair of criteria and its own goodness-of-fit verification. Applied across the global land surface at 0.1°, this amounts to an iterative threshold search and two distributional tests at each of several million grid cells over a 63-year daily record; a substantial and time-consuming computation rather than a single pass over the index. We adopt the procedure from Shan et al. (2024) but apply it directly to the compound SSTCI variate rather than to separate drought and heat series, so that the independence and extreme-value tests are applied to the compound quantity itself rather than to its marginals, a distinction that matters, since well-behaved marginals do not guarantee that events assembled from them are independent or extreme as compound events.
What this yield is a product that is operationally ready in a way a raw thresholding list is not. Thus, for every land grid cell globally, a direct record of discrete events with explicit onset, termination, duration and marginal severity, the form of information most relevant to impact assessment and early-warning applications, where users need to know where and when an event occurred and how severe it was rather than reprocess continuous daily fields.
We agree that coherent spatio-temporal event objects are a scientifically richer construction, and we thank the referee for the suggestion. We would note that a similar product also exists (published in ESSD), where Dheed (Weynants et al., 2025) derive spatio-temporal clusters of dry and hot events from ERA5, and that the two constructions are in any case complementary rather than alternatives. Spatio-temporal clustering operates on per grid cell event records, which are the elements from which coherent event objects are assembled. The present catalogue is therefore not a lesser substitute for clustering but a natural input to it, and one in which the constituent events have already been filtered for independence and extremeness before any clustering is applied. Extending it in this direction involves consequential choices, spatial connectivity criteria, temporal overlap rules, minimum cluster size, that merit dedicated treatment rather than being appended here, and we will note it as a promising direction in the outlook.
We also believe that this allows the data to be easily and readily usable by non-scientific users who would need this for other applications beyond scientific research and operational services.
Comment 6
Given that many different indices exist, I would see potential value in a large ensemble of heat-drought indices, which would also allow quantification of some of the uncertainties involved through the ensemble spread. Such dataset would, in my view, classify as significant.
We agree that a multi-index ensemble quantifying uncertainty through ensemble spread would be a valuable contribution, and we thank the reviewer for highlighting it.
We would note, however, that such an ensemble presupposes the existence of well-documented, validated single-index products to combine. Members must be openly available, standardized, and provided on a common grid before any meaningful ensemble spread can be computed from them, and it is precisely this that is currently lacking for daily, global, soil-moisture-based compound indices. SSTCI is released openly with its code, expressed as a standardized compound variate, and provided on a uniform global daily grid over 1961–2023, properties that make it directly usable as an ensemble member rather than only as a standalone product.
On its own merits, the dataset also fills a gap that no existing openly available resource covers: a daily, global, multi-decadal, soil-moisture-based compound dry–hot index together with an objectively constructed event catalogue. We would suggest that the two things are not in contradiction, providing a well-characterized single product is how the community comes to have an ensemble at all.
In the revision we will position SSTCI explicitly as a ready to ensemble member of a family of compound indices and note ensemble based uncertainty quantification as a direction in the outlook.
Summary
We are grateful to the reviewer for the comments that have materially improved the framing of this work. We hope that, taken together with the clarifications above, the revisions demonstrate that the dataset makes a genuine and non-routine contribution and that it is appropriate for publication in ESSD.
Citation: https://doi.org/10.5194/essd-2026-313-AC1 -
RC2: 'Reply on AC1', Anonymous Referee #1, 17 Jul 2026
I thank the authors for their swift response to my somewhat critical review. I am happy to see that more thought was behind certain decisions than was presented in the manuscript. I encourage the authors to include some, if not all, of their answers in the revised manuscript, as this will considerably improve the readability and help readers to understand the approach. I do still feel that some of the main assumptions, like the use of top 7 cm soil moisture, require a better motivation and possibly a sensitivity analysis. And regarding the conceptualization of soil moisture: readers will wonder why for a compound drought-heat event the treatment of drought should be different from widely accepted approaches including SPI, SPEI, SSMI (which is often calculated at weekly/monthly timescale in addition to daily). I see the authors' rationale for their choice from their response, but less so in the manuscript. It is likely that I would evaluate a manuscript including many of the arguments used by the authors in their reply much more positively, and I encourage the authors to revise their manuscript accordingly.
Citation: https://doi.org/10.5194/essd-2026-313-RC2
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RC2: 'Reply on AC1', Anonymous Referee #1, 17 Jul 2026
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AC1: 'Reply on RC1', Daniel Hagan, 17 Jul 2026
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RC3: 'Comment on essd-2026-313', Anonymous Referee #2, 21 Jul 2026
General comments
Aftab et al. present a new index for quantifying compound dry-hot extremes, the SSTCI. Furthermore, they use it to construct a global event-based discrete catalogue for extreme events, the CDHE. While this is a relevant and important topic, the paper requires further motivation on what makes this particular index novel compared to (and outperforming) the current state-of-the-art. The authors also need to elaborate more on the methodology used to construct the metric. I recommend to replace some of the case studies in the paper with a more thorough evaluation.
Specific comments
- L26: “removal-merging refinement to threshold based spells”: this sentence can surely be rephrased using more comprehensible language
- L54-55: The authors assume the reader already knows all these indices/acronyms (SCDHI, SAPEI, STI, DCDHI, …)
- L57: “drought components in these indices may not fully capture soil moisture variability”: explain why, perhaps the current state-of-the-art needs to be introduced in a bit more detail
- L68: “most traditional drought metrics rely on precipitation signals”: really? What about simple maps of soil moisture anomalies which are very common? And the ET-based metrics you use for comparison further on in the paper?
- L72-78: Repetition of previously made points (temporal scale and precipitation vs soil moisture focus)
- Sect 2.1: It was not very clear what the difference is between mx2t and the more standard 2t, since both appear to be hourly (so it’s not the daily maximum temperature). This is particularly relevant since 2t is available in ERA5-Land at higher resolution, so there would be no need to interpolate it to a finer grid.
- Sect 2.3: For consistency with the other symbols, D in equations 2 and 3 should get a t subscript index. Also, not all symbols are explained (e.g., F_LL, phi, …)
- L144: “Drier than normal”: I don’t understand how this works based on the metric definition; from the text, I understand that the N in equation 2 equals 14, so we only look two weeks into the past? Does this mean the complete time series is not used to compute the SASMI metric? What if it has been dry for two weeks? Or, will the onset of the dry season always appear as a dry extreme? I assume this is a wrong interpretation, but to avoid this, the equations and methodology should be explained more thoroughly.
- L151: Replace “each calendar day” by “a specific calendar day”?
- Sect 2.5: Again, symbols used in the equations are not properly explained, which makes it hard to understand the methodology behind the SSTCI, which is crucial for this paper. What are X, Y, u, v? F is a marginal cumulative distribution, but of what (x or y or something else)? The authors refer to Aftab et al. (2026c) for more details on the methodology, but this is a Zenodo dataset. Hence, I couldn’t find the information to understand the metric. Since it is the main topic of the paper, it should be extended here, in an appendix or in a supplement, but not by referring to someplace else.
- L188: Is the threshold of -2 chosen because the unit of the SSTCI is ‘standard deviations from a normal distribution’? In this case, do you not expect to always detect 2-3% extreme events in each pixel, both in areas where you regularly encounter CDHEs, and those that never experience them? Additionally, it would be good to see some SSTCI histograms to verify the standard normal behaviour.
- Section 3: Some of the later subsections can perhaps go into a supplement.
- L216/220: “managed/agricultural regions”, “land surface controls”: does ERA5-Land account for irrigation/land surface controls?
- L256: Non/significant and small correlations also clearly visible over desert areas like the Sahara. Thoughts?
- Figure 2: The Pearson correlation is not well-suited for time series in which you expect extreme events. Consider using Spearman’s rank correlation as an alternative. Also, a map with an alternative metric could also be useful, e.g., the fraction of times both metrics agree on what is an ‘extreme event’ (by the 2 sigma definition).
- Sect 3.2.2: So the SCDHI is also a daily index? Earlier in the paper, it seemed that the daily resolution was the main novelty of SSTCI. Some more details on SCDHI (e.g., does it also use reanalysis data or is it observation based, what is the spatial/temporal resolution) would be welcome.
- Figure 3: Does panel d show the average of the domain in panels b/c, or a single pixel? Specify in the caption.
- L280: “an event historically distinguished by its unprecedented duration and intensity”: this would of course make it one of the easiest events to test the metric for. Of course, it's fine to showcase with a very obvious event, but I miss insights on how the metric behaves with more intermediate (less “extreme”) events. I also miss any evaluation in terms of false positives: are there any extreme events detected by the method, which were not so extreme in reality? I understand this is not easy to do without any reference, but I make a suggestion in the comment below.
- Sect 3.2.3: Nothing on potential false positives. It would be useful to have something like a confusion matrix for detected CDHEs w.r.t. SASMI, showing their agreement and disagreement percentages. Would be more robust than a limited number of case studies in my opinion.
- Figure 4: The main objective here is to examine the agreement between the different indices. Are there any known events that are not captured by the other metrics, but only SSTCI? This could be a motivation for this new metric (a potential limitation of the other ones), and it would be interesting to better understand the conditions under which it performs better or worse than the current state-of-the-art. In short: it would be useful to assess the inconsistencies between the metrics as well, not only the consistencies.
- Sect 3.3: The second paragraph made me wonder what exactly is the benefit of tracking the compound effect, rather than the two individual effects. A farmer for example would be mostly interested in the drought itself, I think? Perhaps this can be motivated more. Is the SSTCI perhaps in some way quantifying the positive feedback effect drought and heat have on each other?
- L384: “SSTCI provides a complete lifecycle analysis of the event”: in the figure, it drops below -2 twice. It’s not clear to me whether this would therefore be classified as a single event or not. Can the authors elaborate?
- Sect 3.4: Is this procedure only applicable to the SSTCI, or could it be applied to other indices for comparison?
- L412: Are there plans to identify these cases (e.g., conditions that correspond to extreme events), such that they perhaps also can be predicted? Could the SSTCI be ‘forecasted’ in the future using weather forecasts, providing early warnings?
- L424-425: So the moisture/temperature indices in themselves do not show elevated probability of an extreme event post-2000?
- Figure 6: In order to verify whether the whole SSTCI distribution has shifted to more negative values post-2000 (likely, and the hypothesis put forward here), or whether it has increased in variances (which would give the same results), the authors could also compare the occurrence of the “anti-CDHE” conditions, i.e., SSTCI>2. Have these decreased over time?
- Figure 7: I didn’t understand what the slope represents. Why not show box plots like Fig. 6, but with the duration of the events rather than the count?
Technical corrections
- L43: Introduce CDHE acronym
- L91-92: Section numbering for outlook, data availability and conclusions is incorrect
- L558: Figure 9 instead of 10
Citation: https://doi.org/10.5194/essd-2026-313-RC3 -
AC2: 'Reply on RC3', Daniel Hagan, 13 Aug 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-313/essd-2026-313-AC2-supplement.pdf
Data sets
Standarized Soil Moisture Tempreture Compound Index (SSTCI) Rukhshinda Aftab, Daniel Fiifi Tawia Hagan, Guojie Wang, Baoying Shan, Syed Husnain Shah, Zhou Chensi, Wei Xikun, Fareeha Siddique, Ali Hasan Jaffry, and Emmanuel Yeboah https://zenodo.org/records/18280747
Compound dry hot extreme(CDHE) event catalogue Rukhshinda Aftab, Daniel Fiifi Tawia Hagan,Guojie Wang, Baoying Shan, Syed Husnain Shah, Zhou Chensi, Wei Xikun,Fareeha Siddique, Ali Hasan Jaffry, and Emmanuel Yeboah https://zenodo.org/records/20826759
Model code and software
Standarized Soil Moisture Tempreture Compound Index (SSTCI) Rukhshinda Aftab, Daniel Fiifi Tawia Hagan,Guojie Wang, Baoying Shan, Syed Husnain Shah, Zhou Chensi, Wei Xikun, Fareeha Siddique, Ali Hasan Jaffry, and Emmanuel Yeboah https://github.com/Rukhshinda-Aftab/Standarized-Soil-Moisture-Tempreture-Compound-Index-SSTCI-
Compound dry hot extreme CDHE event catalogue Rukhshinda Aftab, Daniel Fiifi Tawia Hagan,Guojie Wang, Baoying Shan, Syed Husnain Shah, Zhou Chensi, Wei Xikun, Fareeha Siddique, Ali Hasan Jaffry, and Emmanuel Yeboah https://github.com/Rukhshinda-Aftab/Compound-dry-hot-extreme-CDHE-event-catalogue-
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Review of “The Standardized Soil Moisture-Temperature Compound Index (SSTCI): Daily scale global data set and event catalogue for compound dry–hot extremes (1961–2023)” by Aftab et al.
The manuscript by Aftab et al. deals with the quantification of compound heat-drought extremes. Given that a large share of climate change impact is believed to happen via increasing occurrence of compound events, this is a highly relevant topic. The manuscript present a new global dataset on the Standardized Soil Moisture-Temperature Compound Index (SSTCI). While a standard dataset on compound heat-drought events is potentially relevant, and the manuscript is generally well-written and illustrated, I am not convinced that the current manuscript provides the major contribution to the field and therefor recommend not to accept the manuscript for final publication in ESSD. My recommendation is based on the following arguments: