the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Jun 2026
Structural warming biases distort extreme rainfall intensification estimates in event attribution
Abstract. Extreme event attribution (EEA) is becoming an increasingly important component of climate change risk assessment and communication. While most EEA methods rely on numerical models, the extent to which model fidelity in representing anthropogenic warming shapes attribution outcomes remains underexplored. Here we identify global-scale biases in leading CMIP6 climate models relative to reanalysis data and show that these biases directly propagate into EEA results. CMIP6 models reproduce the integrated magnitude of anthropogenic warming but systematically distort its three-dimensional structure, underestimating lower-tropospheric warming over land—thus dampening land–sea thermal contrasts—while overestimating upper-tropospheric warming, particularly in the Northern Hemisphere. Consequently, in a storyline-based testbed experiment for the October 2024 Valencia storm (Spain), the response in extreme rainfall rises from ~10 % under CMIP6-derived warming to ~30 % under an observationally constrained signal. This enhanced response is driven by increased low-level moistening, larger convective instability, and strengthened upper-level winds that push precipitation well beyond Clausius–Clapeyron scaling. We also show similar structural mismatches across multiple Northern Hemisphere mid-latitude locations, suggesting that this underestimation is not event-specific. Our results underscore the need to strengthen confidence in attribution methods and provide a robust pathway for constructing observationally constrained counterfactual climates.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-3044', Anonymous Referee #1, 11 Jun 2026
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RC2: 'Comment on egusphere-2026-3044', Anonymous Referee #2, 27 Jul 2026
Main evaluation.
The paper discusses the influence of different perturbations used in the pseudo-global warming experiments to attribute the recent Valancia flood event. The authors compare perturbations derived from CMIP6 models with the perturbation derived from ERA5 reanalyis data, and found that the response is much larger with the ERA5 derived perturbations. They attribute this stronger response to too distinct differences: a much weaker dry lapse rate stabilisation and a stronger land sea temperature contrast in ERA versus CMIP. Given the upcoming field of attribution using similar techniques this is an important and relevant finding that deserves publication. The paper is in general well readable, and figures are clear. However, I also found some fairly major concerns about the interpretation of the results, so I recommend major revisions.
Major:
- The link between changed perturbation is not very clear at the moment. Increased instability (in ERA vs CMIP6) will likely force stronger upward motions (stronger convection), possibly leading to more latent heat release and adjustment of large-scale flow features (as described e.g. in Nie et al., and Lenderink et al. 2017). As the authors rightly point out these dynamical response are relevant when they are are a direct response to the changed humidity content of the atmosphere (or systematic changes in temperature gradients), and can therefore be "attributed" to warming. Yet, I did not find any information on how enhanced stability could lead to a stronger response in the paper, and would be good to at least discuss the mechanism and possibly provide supporting information from the model results
- The second mechanism the authors propose is the adjustment due to the thermal wind balance. Although temperature gradient change due to the surface conditions, I guess that the model quickly adjust temperatures due to the soil scheme, boundary layer turbulence and convection, and the large-scale dynamics. It is therefore a bit hard to understand how the initial perturbation leads to larger rainfall in the ERA5 experiment without more specific information on dynamical changes of the system.
- I am slightly concerned that the sharpening of the surface pressure gradient west of Valancia with warming in the ERA runs (visible in Fig 4 a, c, Fig S10a,c) is responsible/associated with the strong response of rainfall. But, if the authors can show or make it plausible that this response is not directly inherited from the ERA perturbations, but a dynamical response as a indirect results from the perturbation (e.g enhanced moisture and greater instability) this concern could also be a strength of the paper.
- Given the above, I think it would be good to be somewhat more moderate in the claims with respect to how general the results are and how they could be interpreted. It could be worthwhile to also look at discussion in Lenderink et al 2025 in this respect. I would also be a bit more reluctant to the generalise to the other cities, although I agree that the mismatch in lapse rate change is quite important, but many other factors (like land-sea mismatches) could play out differently.
Minor comments:
- l 87: constant relative humidity. Do you use the relative humidity change from CMIP/ERA, and adjust absolute humidity according to temperature and relative humidity change (as is common), or do actually use unchanged relative humidity (so same RH for both C and F experiments).
- Figure 2: It is a bit unsatisfying that these are yearly mean results, and perhaps would be better to show results for the appropriate season (here, or in the supplement)
- Line 275. The link between upper flow and thermal winds looks to be rather speculative
- Line 330. I believe that Engelbrecht et al use ERA to do counterfactual PGW type simulations
- L 360. If I understood correctly, 1-h precipitation is area averaged and are not local intensities, which is a bit of the weird statistic in terms of impact. Did you also looked at small scale hourly precipitation ?
- In general, I think a greater effort could be done to refer to existing literature on PGW experiments.
Engelbrecht, F.A., Steinkopf, J., Chang, N. et al. Extreme event attribution using km-scale simulations reveals the pronounced role of climate change in the Durban floods. Commun Earth Environ 6, 506 (2025). https://doi.org/10.1038/s43247-025-02460-5
Nie J, Sobel AH, Shaevitz DA, Wang S (2018) Dynamic amplification of extreme precipitation sensitivity. Proc Natl Acad Sci USA 115:9467–9472. https://doi.org/10.1073/pnas.1800357115
Lenderink G, Barbero R, Loriaux JM, Fowler HJ (2017) Super-Clausius–Clapeyron Scaling of Extreme Hourly Convective Precipitation and Its Relation to Large-Scale Atmospheric Conditions. Journal of Climate 30:6037–6052. https://doi.org/10.1175/JCLI-D-16-0808.1
Lenderink G, Vries H de, Meijgaard E van, et al (2025) A pseudo global warming based system to study how climate change affects high impact rainfall events. Weather and Climate Extremes 49:100781. https://doi.org/10.1016/j.wace.2025.100781
Citation: https://doi.org/10.5194/egusphere-2026-3044-RC2 -
RC3: 'Comment on egusphere-2026-3044', Anonymous Referee #3, 30 Jul 2026
General comments
The paper investigates the bias of pseudo-global warming signals in widely used CMIP6 climate models and evaluates how this bias influences the counterfactual simulation and therefore the outcome of attribution studies, here exemplarily shown for the extreme precipitation event over Valencia in October 2024. The authors show systematic differences in vertical and horizontal temperature trend patterns between CMIP6 and ERA5 reanalysis as well as differences in thermodynamical and dynamical changes, that are justified by higher vertical stability from weaker vertical lapse rates and low-level moistening as well as by weaker low-level horizontal winds due to a weak land-sea temperature contrast in the CMIP6 simulations. The paper is very well structured and understandably written, outlines methodology and possible caveats in detail and offer important results that should be taken into account by attribution studies. Although the differences in dynamical changes need further explanation, I suggest minor revisions.
Major comments
- The explanation of the differences in dynamical mechanisms with the thermal wind balance is quite vage. Although the land-sea temperature contrast is higher in ERA5, I’m not convinced that this temperature gradient propagate upwards and influences the upper winds in that way, especially because the low levels winds do not show significant changes in CMIP6 or ERA5. In order to stick with this explanation, I suggest to do a more detailed analysis on the actual thermal wind and/or synoptic circulation features that might explain 500 hPa wind differences.
Minor comments
- Line 65: Why did you use SSP2-4.5 instead of SSP3-7.0 for more pronounced signals and still realistic future scenario? Is it the currently most realistic scenario?
- Lines 171-173: 700 hPa (~3km) are rather low- than mid-tropospheric. I suggest to use 500 hPa (~5.5km) for mid troposphere.
- Lines 192-194: It seems that the trend is quite similar between Fig. 2c and f for Iberian Peninsula and no distinct signatures, especially compared e.g. to the US west coast?
- Line 285: Good idea not to nudge. It would be interesting to see, if a comparison to a simulation with nudging would lead to substantial dynamical differences during the event, given that such a simulation would not be too time consuming.
- Lines 363-365: Why should the imposed warming lead to a higher persistence of the event? Especially when an even higher warming (ERA5 future counterfactual) could lead to opposite changes with shorter accumulation duration?
- Fig. 4d-f & Fig. 6a,b: Rainbow colours are not optimal for people with visual disabilities. I suggest a color scale that, of course, can include different colours but with consistently decreasing brightness. Additionally, I would more standardise the labels of the scale instead of jumping from 3 - 5 - 10 - 20 - 30 by almost every second label.
- Fig. 8: I would like to see the dot for Valencia for better comparison as well.
Citation: https://doi.org/10.5194/egusphere-2026-3044-RC3
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- 1
The manuscript by Insua-Costa et al. focuses on how biases in temperature trends extrapolated from CMIP6 models might lead to the development of incorrect counterfactuals for model-based extreme event attribution studies, taking the Valencia floods of 2024 as a relevant example. The focus is particularly posed on the vertical structure of warming, capable of changing the background stability profile when superimposed to simulations of the event under analysis e.g. in the context of pseudo-global warming approaches. Employing an array of different counterfactual simulations, the authors explain how differences in the dynamical and thermodynamical setup of the counterfactual simulation can substantially affect precipitation totals and, consequently, the attribution outcome.
While the thermodynamic arguments provided (increased CAPE, increased IWV with global warming) are consistent with the increase in precipitation expected in a warmer climate at a basic level, the dynamical argument based on thermal wind is very likely flawed. Furthermore, with the exception of the changes in stability in top-heavy vs bottom-heavy warming patterns, there is little physical understanding provided to help the reader connect changes in the vertical warming structure and precipitation outcomes. Given the convective nature of the event and the high degree of interaction with orography, small changes in the circulation can result in very large precipitation differences, with maxima of precipitation decaying or shifting away from the region of interest: this implies that the interpretation of precipitation changes between simulations should be done with great care, taking into account the variability within the ensemble.
The null hypothesis that what has been observed in the Valencia floods was specific to the event, to the simulations, or to the region, and to reasons other than the vertical structure of the warming, is not excluded convincingly. The generalization attempt is, therefore, not supported enough by the result shown. Given that I feel my comments challenge some very central points of the paper, I have to recommend very major revisions or rejection in its current form.
Major comments
1) I believe that the explanation of dynamical changes provided by the authors is not consistent with the basic synoptic meteorology concepts behind thermal wind balance. The explanation provided by the authors for the difference in upper-level flow strength involves changes in the land-sea thermal contrast that would propagate upward and affect free tropospheric winds at 500hPa (Fig. 5f). Thermal wind balance descends directly from the assumptions of geostrophic and hydrostatic balance, and those assumptions are not satisfied at the typical scales of land-sea thermal contrasts. Such temperature gradients originate other types of mesoscale circulation, such as sea breezes, that are non-geostrophic and limited to the height of the boundary layer with no effect on the upper troposphere. Otherwise, we would have a NE/SW oriented jet stream along the western coast of the Mediterranean Sea forming virtually every afternoon during summer as solar heating warms the surface and the ocean stays relatively cool... which is not the case.
2) Knowing the temperature gradient at one layer should not be used to predict the wind several thousand of meters above it, especially in a region with complex orography (rather than in the simplified set up of idealized jet streams over oceans, in which thermal wind is usually introduced during introductory meteorology classes). If the authors would like to continue with the thermal wind argument, they should at least compute the geostrophic and thermal wind and produce actual evidence that specific differences in low-level thermal wind are the dominant contributors to the 10-15% difference in 500hPa wind speed depicted in Fig. 5f, which then might impact vertical shear and convection organisation. This would result in a more mechanistic understanding of why changes in vertical warming structure should affect attribution results.
3) Lacking a clear explanation of the connection between the vertical warming pattern and the wind, one might come to the simpler hypothesis that weaker upper-level winds over the Valencia region are simply due to a lateral shift of the region with the maximum winds. In Fig. 4 the ERA5 counterfactual features a stronger sea-level pressure gradient with a deeper minimum east of Gibraltar, that enhances easterly winds over the southern portion of Spain (Fig. 4c): a similar shift might be present also at 500hPa, and would be consistent with the enhanced precipitation in the Murcia/Almería region visible in Fig. 4f. Please provide 500hPa wind difference maps between simulations to actually show the spatial pattern of wind difference.
4) The explanation of the results is very difficult to understand and at times contradictory. Let us take the sentences at lines 271-274 and the 500hPa wind speed as example, but the lack of clarity is widespread across Section 3.3 and 3.4 and in the Conclusions. Here, we know that the ERA5 counterfactual features an increased land-sea contrast and this would correspond (by the authors' hypothesis) to a weakening of upper-level winds in the counterfactual simulation with respect to the factual (I guess because the thermal wind vector would be roughly aligned with the coastline and point south-westward if land is warmer than the sea, opposing the southerly flow driven by the DANA south of Gibraltar we see in Fig. 4). The C<F is consistent with (F-C)/F > 0, as shown in Fig. 5f. The problem is in the wording used: when the authors write "the ERA5-based perturbations yield a significant strengthening of upper-level wind" (line 272) the reader might be confused, because in the ERA5-based counterfactual the upper-level winds are actually weaker than in the factual. This confusion becomes a contradiction in the conclusions, when it is said "The ERA5-based experiment yields enhanced low-level moistening, stronger convective instability and a more vigorous upper-level jet, producing a super-Clausius–Clapeyron precipitation response". Please revise the explanations to ensure consistency and clarity.
5) The generalization beyond Valencia presented in Section 4.3 is also not convincing, in my opinion. What is shown in Fig. 8 is simply that CMIP6 models, in most cities, tend to feature a upper-level warming bias with respect to the estimate derived by ERA5: such a result is consistent with the bias described by Keil et al. (2021, https://doi.org/10.1175/JCLI-D-21-0196.1) and mentioned in the introduction. However, this does not automatically imply that the precipitation response in London or Los Angeles will follow the same one as Valencia, when a similar approach is applied to study extreme events over those cities. The Valencia event was convectively dominated in the context of a DANA, while extreme precipitation in other parts of the world might be more stratiform and associated with other mechanisms (e.g., atmospheric rivers, cyclones). More physics-based understanding would support better the generalization attempt, also allow to hypothesized for which climatic region the proposed mechanism is expected to be more (or less) relevant.
Minor points
Section 3.1: The global-scale discussion presented in this section is not specifically relevant to the western Mediterranean case study and to the conclusions, and is in this sense a deviation with respect to the point of the paper. Consider removing or drastically shortening, by placing the changes in the western Mediterranean in the context of the North Atlantic/European/Mediterranean region only.
Line 144-145: the role of the lapse rate feedback in inducing Arctic Amplification is being currently reconsidered critically, consider rephrasing this sentence (see Caballero, R., and T. M. Merlis, 2025: Polar Feedbacks in Clear-Sky Radiative–Advective Equilibrium from an Airmass Transformation Perspective. J. Climate, 38, 3399–3416, https://doi.org/10.1175/JCLI-D-24-0031.1.)
Line 171: the 700hPa level is not usually considered "mid-tropospheric", as it still bears substantial influences from the surface (e.g., see the North Atlantic "warming hole"). What about 600 or 500hPa?
Line 193: it looks to me more vertically homogeneous with a relative maximum around 850hPa, while the magnitude of the warming trend near the surface does not really stand out. The CMIP6 trend instead is clearly top-heavy.
Fig. 4: Accumulating precipitation in presence of very extreme peaks might lead to interpretation problems, as changes in single precipitation maxima should not be seen as representative of the behaviour of large area averages. The color scale of precipitation does not increase monotonically, further complicating the interpretation. Please provide spatial precipitation difference maps between simulations to actually show that there is a systematic reduction of precipitation and not a shift or just a loss individual maxima.