the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jul 2025
EXSoDOS 1.0: downscaling of weather extremes shifts for ensemble climate projections using ground-based measurements, reanalysis and stochastic modelling
Abstract. Accurately representing the changes of local extreme weather events in climate projections is crucial for climate impact assessment and adaptation services. Climate models often struggle with capturing these events due to their coarse spatial resolution. Existing downscale products successfully reduce overall biases of past or future climateological variables, but the representation of variability and extreme events including their past and future shifts under climate change are still not addressed. A new stochastic model, EXSoDOS, addresses this gap by the DOwnScaling of weather EXtremes Shifts for ensemble climate projections using ground-based measurements, reanalysis, and global climate models. This is done by using a stochastic model that correlates coarse-scale gridded historical climate records with the point-scale measurements. Therefore, EXSoDOS combines ground-based data (either from the Global Historical Climatological Network or user-specified), ERA5 reanalysis, and global climate model (GCM) projections to downscale past and future daily climate records. We demonstrate EXSoDOS for 5 use cases, resp. daily minimum temperature in Belgium, daily maximum temperature in Azerbaijan, heat stress in India, wind velocity in Germany and precipitation in Mali. It is found that EXSoDOS is able to represent annual cycle variability, density distributions, and extreme events of return periods of up to 10 years, while they are all underrepresented by the raw GCM outputs. Observed tendencies towards more extremes between two past periods 1961–1990 and 1991–2020 are also better represented. Projections under the SSP585 scenario suggest amplified extremes in maximum temperature, precipitation, and heat stress by 2071–2100. Furthermore, downscaling affects the outcomes of shifting extremes under future climate change, which is evident in terms of both absolute and relative changes, as well as changes in return periods. While limitations of statistical downscaling persist, it is concluded that EXSoDOS offers a novel method for estimating past and future shifts in weather extremes for weather stations with a sufficient daily record of data of multiple decades.
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CC1: 'Comment on egusphere-2025-2214', Rasmus Benestad, 03 Sep 2025
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Interesting paper. I have some thoughts and comments that I'd like to share.
In the abstract, there is the statement "Existing downscale products successfully reduce overall biases of past or future climateological variables, but the representation of variability and extreme events including their past and future shifts under climate change are still not addressed" which I don't think is a bit misleading. Have the authors examined all scientific publications and really can claim that there are no papers addressing "extreme events including their past and future shifts under climate change" or is it a bit premature? What about e.g. https://doi.org/10.5194/hess-29-45-2025 that presents downscaled information about heavy daily precipitation or https://doi.org/10.5194/ascmo-4-37-2018 addressing heatwaves? I also think that dynamical downscaling within CORDEX has addressed extremes.
I think that the claim "While limitations of statistical downscaling persist, it is concluded that EXSoDOS offers a novel method for estimating past and future shifts in weather extremes for weather stations with a sufficient daily record of data of multiple decades" is not justified when the paper has not examined the whole spectrum of empirical-statistical downscaling. It has for instance ignored the works from the Norwegian statistical community over several decades and has contributed to pioneering the downscaling of statistical properties.
Comment to "heavy precipitation, heavy wind, extreme heat (stress) and cold spells are generally underrepresented in climate projections" - it's important to keep in mind what the models are designed to represent. The numerical models such as GCMs do have a minimum skillful scale (doi:10.2151/jmsj.2015-042) and are not expected to produce the numbers that represent e.g. local rainfall caught in a rain gauge or local temperature measured by a thermometer. The number produced by a model and measured by an instruments usually represent different aspects of a condition. This paper could definitely improve by providing a better account on downsclaing and a more accurate context. Furthermore, efforts with AI/ML for downscaling have lots to learn from empirical-statistical downscaling in the past and I'd recomment reading up on the subject before making claims about the merit of AI/ML.
I dont think that the reference (Eyring et al., 2016) deals with downscaling, but it presents the global models. Thus the reference here may be a bit misleading. Again, the cited literature on empirical-statistical downscaling is very unimpressive and this makes the claims about the merit of the proposed method hyperbole.
Citation: https://doi.org/10.5194/egusphere-2025-2214-CC1 -
AC1: 'Reply on CC1', Hendrik Wouters, 08 Sep 2025
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We thank you for your interest in our work and your recommendations. We agree that your related studies should be referenced, and we will include them upon revision.
As you note, the methods in the cited papers are designed to downscale overall statistical descriptors such as probabilities of heavy rainfall, IDF curves, or heatwave probabilities, which are important to provide insights for climate change assessments. In contrast, the focus of our work is on generating sequential, downscaled daily time series of various meteorological variables representing extremes by correlating coarse-scale climate records with point-scale observations. Such continuous time series are essential for driving climate impact models sensitive to variability and extremes (e.g., crop models). We already acknowledge similar work on downscaling (see references at r32–48) which also includes dynamical downcaling by CORDEX. To our best knowledge, our claims about gaps and novelty mentioned here hold.
While the paper gives the necessary context on the type of downscaling addressed here, we admit that the mentioned phrases may lead to confusion. We will reformulate these sentences upon revision, and better clarify the distinction with to other types of downscaling.
We agree that it’s more correct to state that GCMs are not designed to represent local extremes as observed in measurements than stating that they underrepresent local extremes, as they are generally capable of representing of what they are designed for, namely climatological variables on large scales. We will reformulate the quoted sentence upon revision.
Finally, we note that Eyring et al. (2016) was cited only to provide context on global climate projections, not to point to downscaling products. We fully agree that AI downscaling can learn about existing statistical approaches, as we will also hightlight upon revision.
Citation: https://doi.org/10.5194/egusphere-2025-2214-AC1 -
CC2: 'Reply on CC1', Boucary Dara, 08 Sep 2025
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Nous vous remercions sincèrement pour vos commentaires détaillés et constructifs, qui nous aident à mieux situer nos travaux dans le contexte plus large de la recherche existante. Vous avez tout à fait raison de souligner que de nombreuses études antérieures ont déjà abordé les événements extrêmes et leur évolution future dans le contexte du changement climatique, par des approches statistiques et dynamiques, notamment dans le cadre de CORDEX. La formulation de notre résumé a pu donner l'impression que ces aspects n'avaient jamais été pris en compte, alors que notre intention était de faire référence plus spécifiquement aux zones hétérogènes correspondant à nos régions d'étude. Nous réviserons cette section afin de reconnaître explicitement ces contributions importantes, y compris celles que vous avez mentionnées (par exemple, sur les fortes précipitations et les vagues de chaleur). Nous prenons également note de vos observations sur la littérature concernant la réduction d'échelle statistique, en particulier les travaux menés par la communauté norvégienne. Nous allons approfondir notre revue de la littérature et ajuster certaines affirmations afin d'éviter toute affirmation excessive. Enfin, vos remarques sur la distinction entre les résultats des modèles numériques globaux et les observations locales sont particulièrement pertinentes. Nous intégrerons cette clarification afin de mieux cerner les limites et les objectifs de notre approche. Nous vous remercions encore une fois pour vos commentaires constructifs, qui nous aideront à améliorer considérablement la clarté et la portée de notre article.
Citation: https://doi.org/10.5194/egusphere-2025-2214-CC2 -
CC3: 'Reply on CC2', Boucary Dara, 08 Sep 2025
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We sincerely thank you for your detailed and constructive comments, which help us to better situate our work within the broader context of existing research.
You are absolutely right to point out that many previous studies have already addressed extreme events and their future evolution in the context of climate change, through both statistical and dynamical approaches, notably within the CORDEX framework. The wording in our abstract may indeed have given the impression that these aspects had never been considered, whereas our intention was to refer more specifically to heterogeneous areas corresponding to our study regions. We will revise this section to explicitly acknowledge these important contributions, including those you mentioned (e.g., on heavy precipitation and heatwaves).
We also take careful note of your observations on the literature regarding statistical downscaling, in particular the work carried out by the Norwegian community. We will expand our literature review and adjust certain statements to avoid any exaggerated claims.
Finally, your remarks on the distinction between the outputs of global numerical models and local observations are particularly relevant. We will incorporate this clarification in order to better frame the limitations and objectives of our approach.
Once again, Thank you for your constructive comments, which will help us significantly improve the clarity and scope of our article.
Citation: https://doi.org/10.5194/egusphere-2025-2214-CC3
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CC3: 'Reply on CC2', Boucary Dara, 08 Sep 2025
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AC1: 'Reply on CC1', Hendrik Wouters, 08 Sep 2025
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