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https://doi.org/10.5194/egusphere-2026-2465
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2026-2465
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
13 May 2026
| 13 May 2026
Status: this preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Subseasonal prediction of compound heat and drought events
Abstract. Compound heat and drought events have severe socio-economic impacts on human health, agriculture and electricity supply. While these compound extremes are projected to intensify under climate change, our understanding of their subseasonal predictability remains limited compared to that of individual heat or drought events. In this study, we evaluate the predictability of compound heat and drought events over Europe using the subseasonal prediction system of the European Centre for Medium-Range Weather Forecasts (ECMWF). We find that the physical coupling between heat and drought contributes up to 10 % towards an increase in forecast skill when heat and drought co-occur, relative to a baseline that assumes independence between extremes. However, in regions where the physical coupling between heat and drought via land-surface interaction is misrepresented, compound skill can be lower than when drought are predicted in isolation. These findings highlight the critical role of accurately simulating land-surface feedbacks to improve the reliability of the subseasonal prediction for compound extremes.
How to cite. Wu, R. W.-Y., Büeler, D., Pyrina, M., Day, J., Imamovic, A., Humphrey, V., and Domeisen, D. I. V.: Subseasonal prediction of compound heat and drought events, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-2465, 2026.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Status: open (until 24 Jun 2026)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
- RC1: 'Comment on egusphere-2026-2465', Anonymous Referee #1, 04 Jun 2026 reply
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RC2: 'Comment on egusphere-2026-2465', Anonymous Referee #2, 18 Jun 2026
reply
Major Comments
- The conclusion suggests that the soil moisture–temperature (SM–T) coupling is underestimated, while the soil moisture–precipitation (SM–P) coupling is overestimated. Given that temperature and precipitation are not independent variables, I would appreciate a more in-depth discussion of the physical mechanisms that could lead to such contrasting biases. In addition, it would be helpful if the manuscript explicitly defined what processes are represented by the SM–T and SM–P coupling metrics. Relevant discussions in Santanello et al. (2018) may provide useful context.
- Line 230: The manuscript states that the high predictability of heat contributes to improved prediction of compound events. Could the authors clarify the mechanism behind this statement? Also, does the "heat" category include compound heat–drought events, or does it refer only to heat-only events? This distinction is important and should be explicitly stated in the manuscript.
- Would the results shown in Figures 1–4 remain qualitatively unchanged if GPCP precipitation were used instead of ERA5 precipitation? Since Figure 5 is linked to the interpretation of precipitation-related land–atmosphere coupling processes, using GPCP consistently throughout the analysis may provide a more coherent framework.
- The discussion focuses on regions where compound-event predictability is degraded by misrepresented land–atmosphere coupling. Conversely, do regions with relatively high predictive skill also exhibit more realistic representations of the land–atmosphere coupling processes shown in Figure 5? Additional discussion on this aspect would strengthen the manuscript. For example, it may be useful to examine regions east of Eastern Europe and parts of Western Europe where skill is comparatively high.
Minor Comments
- Section 2.1: I am having difficulty understanding the centered 30-day approach used to construct the climatology. Is this methodology different from that used in previous subseasonal forecast skill studies? The analysis focuses on Weeks 1–4, which implies that only lead days 1–28 are considered, whereas the climatology appears to be calculated using a centered 30-day window. If the monthly-mean anomaly is computed from a 28-day forecast period but referenced against a 30-day climatology, the comparison may not be entirely consistent. Please clarify this methodology and consider using matching periods if necessary. In addition, please clarify whether "aggregation" refers specifically to averaging, or whether it may also involve summation.
- Lines 40–44: While I understand the point being made, I am not sure why this discussion is needed in the current context. The connection to the main objectives of the manuscript is not immediately clear.
- Lines 78–79: This sentence appears to fit more naturally at the end of the first paragraph. Unless the methodology is highly specialized, it may be more reader-friendly to briefly describe the approach (2–3 sentences) rather than only referring the reader to previous studies.
- Equation 1: Please define the indices i, j, n, and y explicitly in the text.
- Lines 110–117: This paragraph may fit more naturally immediately after Line 95 or Line 101.
- Equation 3: The formulation appears different from that used in previous studies. Could the authors explain why the additional term following ((p_k-o_k)^2) is included?
- Lines 142–151: This appears to be one of the most novel aspects of the manuscript, but I found it difficult to follow. Please provide additional explanation regarding the physical and statistical interpretation of (BSS_{CHD_ind}) and how it should be interpreted relative to the standard (BSS_{CHD}).
- Figure 4: It may be helpful to reverse the FAR color scale so that the visual interpretation is consistent with HR (i.e., colors associated with better performance correspond to the same direction in both panels).
Citation: https://doi.org/10.5194/egusphere-2026-2465-RC2
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Latest update: 22 Jun 2026
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Department of Applied Physics and Applied Mathematics, Columbia University, New York, USA
Dominik Büeler
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Federal Office of Meteorology and Climatology MeteoSwiss, Zürich, Switzerland
Center for Climate Systems Modeling, C2SM, Zürich, Switzerland
Maria Pyrina
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Center for Climate Systems Modeling, C2SM, Zürich, Switzerland
European Centre for Medium-Range Weather Forecasts, ECMWF, Bonn, Germany
Jonathan Day
European Centre for Medium-Range Weather Forecasts, ECMWF, Reading, UK
Adel Imamovic
Federal Office of Meteorology and Climatology MeteoSwiss, Zürich, Switzerland
Vincent Humphrey
Federal Office of Meteorology and Climatology MeteoSwiss, Zürich, Switzerland
Daniela I.V. Domeisen
Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland
Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland
Short summary
Compound heat-drought events threaten health, agriculture, and energy. Using ECMWF sub-seasonal hindcasts, we find that physical coupling increases forecast skill by ~10 % relative to assuming event independence. However, skill drops significantly where land-atmosphere interactions – namely soil moisture feedbacks – are misrepresented. We identify European regions where these forecasts are most reliable and highlight physical biases as the primary barrier to accuracy elsewhere.
Compound heat-drought events threaten health, agriculture, and energy. Using ECMWF sub-seasonal...
The manuscript "Subseasonal predictability of compound heat and drought events in Europe" by Wu and co-authors presents the predictability of compound heat and drought events in summer by the ECMWF models. The article is concise and dedicates most of the space to introduce clearly the methods used to evaluate the predictability and the role of the atmosphere-land coupling is the skill obtained. I think the article is well structured, clear and fits in the scope of the journal. I only have minor comments to the authors:
Appendices: Could you please reorder the appendices (first C, second B, third A, fourth D) so the first one introduces is number A?
Figures C1-C3: Could you please reduce the blank spaces between figures (this also applies to figure 1, 4 and 5)
Line 131: I'd rather use forecast as the past participle, instead of forecasted
Lines 209-210: Interestingly, this only happens for positive biases. Regions with negative biases show fair skill. Any comment on this?
Figure 6: Could you put the legend outside panel a and increase its font size? Could you also increase the font size of correlations in all panels? I struggle to read the values there. This also hold for figure D1