| 10 Nov 2025
A warming adjustment method for CORDEX RCM simulations
Abstract. Regional climate model (RCM) projections provide high-resolution information essential for climate impact assessments and adaptation planning. Currently available Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations, driven by CMIP5 global models, exhibit considerably weaker European warming throughout the 21st century compared to the latest Coupled Model Intercomparison Phase 6 (CMIP6) models. This discrepancy arises from multiple factors: RCMs tend to underestimate warming compared to their driving models in part due to the use of aerosol climatologies, while CMIP6 models themselves exhibit higher climate sensitivity than their CMIP5 predecessors. Here, we present a method to adjust existing CORDEX simulations toward the large-scale warming simulated by CMIP6 models while preserving fine-scale spatial structure and physical coherence. The method operates by reassembling complete annual fields from the original simulations according to smoothed temperature trajectories, maintaining temporal monotonicity without requiring interpolation. We demonstrate its application to both temperature and precipitation, including mean conditions and extremes over Europe, though the approach is applicable to any regional domain and warming-sensitive climate variable. As an extension, the method can be applied sequentially to establish consistency from prescribed global warming levels through CMIP6 regional patterns to high-resolution RCM projections. This bridges the temporal gap between regional and global model development cycles, making existing high-resolution climate information compatible with both updated model generations and specific warming targets.
Competing interests: Sonia I. Seneviratne is a member of the editorial board of Earth System Dynamics. The authors declare that they have no other competing interests.
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.
Review of “A warming adjustment method for CORDEX RCM simulations” by Stefanie Börsig, Dominik L. Schumacher, and Sonia I. Seneviratne
In this paper, the authors propose a novel approach to combine the results of EURO-CORDEX regional climate models (RCMs), which are forced by CMIP5 global climate models (GCMs), with the results of CMIP6 global climate models, as constrained by observations in the IPCC AR6, to provide more accurate and up-to-date regional climate information. The RCMs indeed tend to underestimate regional warming compared to global climate models (both CMIP5 and CMIP6), as documented in the literature. However, they still provide useful high-resolution climate information over Europe.
The authors aim to produce regional climate transient projections that are consistent with global climate models and, ultimately, with the constrained CMIP6 projections proposed by the IPCC. This involves a two-step process where global projections are first matched with the constrained IPCC projections, and then the regional projections are matched with the global projections.
The method consists of stitching together complete annual fields from the RCM simulations trying to temporally match the warming simulated by the GCMs. This approach ensures spatial and inter-variable consistency.
The authors describe the method and then illustrate its interest, for changes in temperature, precipitation, and their extremes.
This paper presents an innovative and practical methodology with clear potential applications, for climate services for example. While the paper is interesting and generally well-written, several aspects of the methodology lack sufficient clarity, making them difficult to follow. Additionally, the discussion of the method’s limitations remains underdeveloped.
Major revisions are required to address these issues before the paper could be published.
Major comments
The method is not always clearly explained, and the details are sometimes difficult to follow. The authors should therefore improve the description of the method (see the minor comments for the points where improvements are particularly necessary).
The limitations of the method concerning snow cover, and the estimation of cold, dry, or wet spells in winter, which could be disrupted when years are reassembled, should be better discussed and illustrated. For example, snow cover in January will have nothing to do with the snow cover from the previous December, which could be a major issue for some applications. Also, since some RCM years can be repeated, what is the impact on temporal variance at different time scales: daily, seasonal, and decadal? The authors should analyze at least some of these limitations, and discuss all of them thoroughly.
As I understand, the authors use standard calendar years (January to December), but it would make more sense to at least start the year in December so that the winter averages in the regional projection retain climatological meaning (i.e., to avoid stitching December to January and February from another winter). At the very least, the snow season would not be artificially split. More generally, I think that for many applications, using "hydrological years" (e.g., years starting in October for example) would help avoid issues related to snow and reduce problems related to soil moisture. What is the rationale for using standard years?
The method also makes the implicit assumption that the regional warming of GCMs over Europe is accurate (for a given level of global warming), but an increasing number of studies suggest this is not the case, i.e. that GCMs themselves tend to underestimate warming over Europe. The method proposed in this paper corrects for the fact that CMIP6 models, as an ensemble, may overestimate global warming, but it does not account for the possibility that they underestimate warming over Europe. As a result, the global "cold" correction may amplify the regional underestimation of warming in the final adjusted projections. This should be noted and discussed.
Minor comments
l48-52. This paragraph is not very clear. It could be useful to better explain the method of Tebaldi et al. (2022), as it is used here and at other places as a reference for the explanation of the authors’ method. For example, "harmonizing existing high-resolution regional climate projections with updated global and regional warming constraints" is not clear or precise.
Introduction: I think it would be useful to cite Ribes et al. (2022), who show another line of evidence for the underestimation of regional warming over Europe (France in that case) by RCMs (and the GCMs). Also, it would be interesting to discuss how climate services across Europe try to deal with the issue of reconciling global and regional projections (e.g., Corre et al., 2025, for France). I also think that the approach of Corre et al. has the advantage of taking into account the underestimation of regional warming over Western Europe by all models (global and regional), compared to the approach proposed by the authors. This could also be discussed in the conclusion, for example.
Corre et al. (2025) Using regional warming levels to describe future climate change for services and adaptation: Application to the French reference trajectory for adaptation. Climate Services
Ribes et al. (2022) An updated assessment of past and future warming over France based on a regional observational constraint. Earth Syst. Dyn
line 72. SSP5-8.5 and RCP8.5 are not exactly identical, which can lead to some differences. The authors should discuss this and cite relevant literature.
l82. The authors should provide the exact properties of the locally weighted regression: which order is used? (e.g., linear or quadratic?).
l87. Which markers? They are hard to see in the figure. Also, I don’t see repeated years at the beginning of the mapping. The RCM years seem to increase from the beginning to 2075, so why are they filtered (in grey) at the beginning? The authors should clarify this point.
l89-92. Not clear. Also, I don’t understand exactly how the linearization works or why a long window of 20 years is needed. Also, the evolution of years in Fig 3c3 does not seem totally linear over 20 year periods. The sentence on line 91 that justifies this is not clear. And is there not a risk of inaccuracy by forcing all the 20-year lines to connect. ? Additionally, the regression does not provide entire years I suppose, but years with decimals (e.g., 1998.54). Please explain how this is dealt with.
Figure 1c (panels 3 and 4). It is better to avoid placing the legend over the curves.
l102. Is it always possible to enforce a 0.75 slope in the GCM-RCM mapping? If the RCM warms much slower than its driving GCM, how is it possible to maintain a 0.75 slope? What happens if it is not the case?
l104-107. Not clear. And what is the scientific justification for this? Why do the authors avoid consecutive duplicate years if it is the best choice to match the GCM?
Near l125. The limitations of the method could be better explained. If I understand correctly, 31/12/2012 could be followed by 01/01/2011 or 01/01/2013. In any case, there is a discontinuity, with possible large and unphysical shifts in snow cover (as we are in winter). This should be more clearly mentioned (rather than referring vaguely to “memory issue”). Would it not have been preferable to start years in fall, for example, to limit this issue with snow cover (and soil moisture) i.e. to use "hydrological years"? Also, if someone is interested in cold, wet, or dry spells in winter, this will be problematic. This should be noted.
I’m not sure to understand the point of section 2.3, as no result of the “validation” described is shown.
l130. Is it not an issue to have to truncate the adjusted time series? Information from the same ensemble of RCMs cannot be used consistently through time periods because each regional climate simulation has to be truncated at a different time. Is this not an issue, for example, when characterizing model uncertainties, as the ensemble of models considered changes with time? Please also provide a table (or a figure) with the last (and first) year of each adjusted RCM simulation.
For example, how can the adjusted time series in Figure 2 be extended to 2100? In Figure 4, when the text refers to 2060, what is the exact time period represented? Is it for example a 20-year average (e.g., 2050–2070). Are all the adjusted regional projections available until 2070, or is there a cutoff earlier for some simulations? Is it not an issue?
l136. What is the size of the window to compute the slope here? Is it 20 years also? Are they running windows?
Figure 1. The numbering is inconsistent, with panels labeled as a, b, and then c1, c2, c3, etc.
L150–170. It is not really clear how the adjustment for global warming is done. The explanation should be detailed, explained step by step.
Also, when the adjustment is applied to the 95th percentile from the IPCC estimate, is the ensemble mean of CMIP6 models adjusted to the 95th percentile? If so, the resulting multi-model distribution extends well above the IPCC 95th percentile, which does not really make sense, I think. Am I misunderstanding something?
Note: Figure 3 is not particularly clear (especially 3a). Also, please add the ensemble spread in Figure 3c with shading.
Conclusions. Please discuss the limitations of the method more thoroughly in the conclusion.
I also found quite a few typos. A careful proofreading prior to the submission of the new version of the manuscript could be beneficial.
“chatateristics” (l164)
“trajectoeies” l165
“warming-adjustement” (Fig. 3 caption)
“tragectory” (Fig. 3 caption)
“warming responsedatasets"” (Fig. 4 Caption)
“temperatues” (Fig. 4 caption)
“unter” (Fig. A1 caption)
Figure A1, title: 20250 instead of 2050
“componentn” (Fig. A2 caption)