the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Jun 2026
Contrasting Detection and Attribution of Temperature and Precipitation Changes in the Western Mediterranean from CMIP6 DAMIP Experiments
Abstract. The Western Mediterranean (WM) is a recognised climate change hotspot where regional temperature and precipitation trends reflect the interplay between anthropogenic forcing and strong internal variability. In this study, the detection and attribution of seasonal temperature and precipitation changes during 1951–2020 across climatically derived subregions of the WM is investigated using a multi-method framework and CMIP6 DAMIP single-forcing experiments. Prior to attribution, models were evaluated according to their ability to reproduce the observed spatial structure of regional trends, and a performance-based subset was selected for the analysis. Detection and attribution were assessed using complementary approaches including the signal-to-noise ratio (SNR), the fraction of attributable risk (FAR), distribution-based comparisons, and an optimal fingerprinting additive decomposition framework. Results reveal a robust anthropogenic imprint on temperature trends across the WM. Forced temperature signals emerge clearly from internal variability in all subregions, with FAR values exceeding 0.95 in most cases. Greenhouse gas forcing is identified as the dominant driver of the observed warming, especially in summer, while anthropogenic aerosols exert a compensating cooling influence that partially offsets it. In contrast, precipitation trends remain largely within the bounds of internal variability. None of the detection approaches identify a robust externally forced precipitation signal at the regional scale, and attribution results suggest that internal variability remains the primary driver of observed precipitation changes during the study period. These findings highlight the importance of subregional-scale attribution and model performance filtering in reducing uncertainties, providing a basis for future attribution studies in this highly vulnerable region.
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Status: open (until 06 Aug 2026)
- RC1: 'Comment on egusphere-2026-2830', Davide Faranda, 09 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-2830', Diego Urdiales Flores, 20 Jul 2026
reply
First of all, I would like to thank the Editor and the Authors for the opportunity to review this interesting and well-motivated paper. I learnt a lot about how the models were selected to create the subset and DAMIP experiments.
The paper addresses an interesting topic, namely the Contrasting Detection and Attribution of Temperature and Precipitation Changes in the Western Mediterranean from CMIP6 DAMIP Experiments. This subject has not been extensively investigated. Therefore, this study represents a valuable contribution to existing literature. However, the manuscript requires major revisions.
The motivation for this study is clear, and the conclusions are both interesting and supported by a novel methodology and set of metrics. However, I have some concerns regarding the presentation of the methodology and results. In particular, it is challenging to follow the methodological workflow and the progression of the results throughout the manuscript. Furthermore, precipitation analysis requires careful assessment, particularly in subregions 2, 3, and 5, where observed and historical trends exhibit opposing signals, specifically in summer.
Major comments:
- The models exhibit larger biases in precipitation estimates, particularly over subregions 2, 3, and 5 (Fig. 1h and Fig. 8b). Morin 2011, based on GPCC observations (https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2010WR009798), identified statistically significant precipitation trends over the Mediterranean basin (see Fig. 1b). These areas also show large relative precipitation changes when normalized by mean annual precipitation (Figs. 4a and 4b in Morin, 2011). Therefore, it would be valuable to further discuss the reasons why the models exhibit larger biases in these regions and whether their inability to reproduce these observed trends is related to model limitations, internal variability, or the representation of regional precipitation processes.
- The signal-to-noise ratios in Figs. 7a and 7b remain between −0.5 and 0.5 for all subregions, suggesting weak attribution signals. This may be influenced by the length of the record or by the magnitude of the detected trends. Please discuss this issue in relation to Morin (2011) (see Figs. 5 and 6). Considering that the analysis uses a 69-year record, the minimum detectable absolute trend should be around ~10 mm/decade (see Fig. 7 in Morin, 2011). The authors should clarify whether the low signal-to-noise ratios arise from record-length limitations or other factors affecting trend detection.
- I suggest including a flowchart that provides a simple overview of the methodology, as well as the associated metrics, tests, and the processes used for the detection and attribution of temperature and precipitation changes. Such a figure would help readers navigate the manuscript more easily and follow the progression of the analysis without having to repeatedly move back and forth between sections.
- The Discussion section could be divided into two subsections: (1) the advantages and limitations of climate change detection using CMIP6 DAMIP models, and (2) the attribution of the detected changes across the nine subregions of the Western Mediterranean. This structure would allow for a clearer assessment of both the strengths and limitations of the modeling framework, while also highlighting regional differences in the attribution of climate change signals.
- I suggest including a map as the final figure that summarizes the key metrics, statistical tests, and drivers responsible for detecting and attributing changes in precipitation and temperature. The map should highlight the nine subregions of interest and emphasize that, although all these areas are part of the Western Mediterranean region, they are influenced by different climate-change drivers and therefore exhibit distinct patterns of change.
Minor comments:
- I suggest using different color palettes in Fig. 5 and Fig. 7: one palette to represent the subregions (a,b) and another palette to represent the attribution categories (c,d).
- Figures 5a,5b, 7a,7b: The trends are difficult to distinguish clearly. Consider adding a zoomed-in inset for the 2005–2015 period to better illustrate the differences and the divergence among the subregional trends.
- Figures 5c and 5b are predominantly characterized by attribution category 2 (detectable and attributable increase). Please consider using a classification scheme ranging from −4 to 4, including the corresponding category names or acronyms. Furthermore, if subregional differences are identified, clarify why all subregions exhibit dominance of the same attribution category.
- Figures 7a and 7b: Please consider restricting the y-axis range to −2 to 2 in order to better highlight the differences among the subregions. Additionally, please discuss the results presented in Figs. 7c and 7d, particularly regarding the occurrence of attribution categories −3, −2, 0, 1, and 4. Explain the possible mechanisms or factors that lead to the dominance of these categories.
Citation: https://doi.org/10.5194/egusphere-2026-2830-RC2
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- 1
The manuscript applies four complementary detection and attribution diagnostics (SNR/ToE, Knutson & Zeng categorical classification, FAR, and Ribes et al. additive-decomposition optimal fingerprinting) to seasonal temperature and precipitation trends over 1951-2020 in nine sub-regions of the Western Mediterranean, using CMIP6 DAMIP single-forcing experiments and a performance-based model subset. The headline result, a robust GHG-dominated warming signal against a precipitation signal that cannot be distinguished from internal variability, is methodologically careful but is not in itself a new finding for the region.
My main recommendation is that the authors either sharpen what is genuinely new in this analysis or extend it in a direction that adds new information, specifically toward extremes and circulation-conditioned attribution, both of which the manuscript only gestures at. The methodology is sound and the multi-method cross-validation is a strength, but the manuscript currently reads as a careful, more granular confirmation of already-known results. I recommend major revisions, focused on (i) clarifying the novelty claim, (ii) extending the analysis to temperature extremes (and precipitation extremes if feasible) using the same DAMIP framework, and (iii) at minimum discussing, and ideally testing, whether circulation-conditioning changes the precipitation attribution conclusions, with the literature suggested below.
Major comments
1. The result that Mediterranean/European warming is robustly attributable to GHG forcing, partially offset by aerosols, is already established (e.g. Stott, 2003; Feng et al., 2022, both cited). Likewise, the conclusion that Mediterranean precipitation trends stay within the envelope of internal variability is already well documented, including by some of the present authors (Campos et al., 2025, JGR Atmospheres, same lead author, same sub-regional clustering) and by Vicente-Serrano et al. (2025, Nature) and Seager et al. (2025), all cited in this manuscript. The present study's contribution is therefore methodological refinement (sub-regional resolution, DAMIP decomposition, model screening, multi-method cross-checking) rather than a new substantive result. That's a defensible contribution, but it should be argued explicitly: what conclusion follows from this finer-grained analysis that could not already be drawn from Campos et al. (2025) or the broader literature? As written (L86-98), the novelty claim rests on "DAMIP being under-utilised" and "a consistent framework enabling direct comparison," which is a methodological framing, not a scientific one. I'd ask the authors to state directly, e.g. in the Conclusions, one or two concrete things we now know about WM hydroclimate that we didn't before this study.
Two literature pointers the authors may want to add, neither currently cited:
2. All diagnostics in this paper (Sect. 2.1-2.3) are built on monthly-mean temperature and precipitation, aggregated to seasonal Sen's-slope trends. No extreme indices (TXx, warm-spell duration, Rx1day, R95p, consecutive dry days, etc.) are computed anywhere. This matters for two reasons. First, the attribution literature increasingly finds that the anthropogenic signal in extremes, particularly temperature extremes, emerges more clearly and earlier than in seasonal means, because thermodynamic (Clausius-Clapeyron-type) responses are generally better constrained across models than the dynamical/circulation responses that dominate mean precipitation trends. Second, the authors themselves seem to recognise this: the very last paragraph before the Conclusions (L600-603) states that "future work should also further investigate other aspects of sub-regional climate change in the WM, particularly the behaviour of extreme events under continued global warming and their links to changes in atmospheric dynamics and physical processes," but this is offered as a single deferred sentence rather than pursued. Given that the DAMIP single-forcing runs used here are typically archived at daily resolution, at least a temperature-extremes analogue of the existing Sect. 3.2 (e.g. SNR/FAR for TXx or a heatwave-magnitude index) seems within reach with the data already assembled, and would meaningfully strengthen the paper's claim to novelty (point 1 above). I'd consider this the single highest-value addition to ask for.
3. SNR, FAR, Knutson-Zeng classification, and Ribes optimal fingerprinting all test for a shift in the marginal distribution of trends. None of them ask whether the (non-)detection of a forced precipitation signal depends on the realised atmospheric circulation. This isn't a minor omission for the WM: several of the manuscript's own citations point directly at circulation as the channel through which forcing affects precipitation. Seager et al. (2025), cited, argue that the observed Mediterranean drying via a positive NAO trend over the last 65 years is "an extreme outlier" relative to the CMIP6 ensemble, i.e. the disagreement between models and observations may be a dynamical (circulation) problem rather than evidence that internal variability alone explains the trend. Olmo et al. (2024) and Olmo et al. (2025), both cited, both by co-authors of this manuscript, already provide circulation-regime classifications and circulation-based model filtering for the Euro-Mediterranean. None of this machinery is brought to bear on the attribution problem here; the model-selection step in Sect. 3.1 filters on spatial-pattern correlation of trends, not on circulation fidelity.
I'd recommend the authors either (a) repeat at least one diagnostic (e.g. FAR, or the additive decomposition) stratified by circulation regime/season-type, using the regime classification from Olmo et al. (2024), to test whether the "non-detection" of a forced precipitation signal is a pooling artefact that disappears once circulation is controlled for; or (b) at minimum, discuss this as a methodological limitation in Sect. 4, citing the conditional/storyline attribution literature that reaches different, sometimes opposite-signed, conclusions for Mediterranean precipitation extremes when conditioning on circulation:
Points 2 and 3 together suggest a coherent path forward: extend the attribution framework from seasonal means to (a) extreme indices and (b) circulation-conditioned subsets, for temperature in particular. This would both address the novelty concern (point 1) and respond directly to the authors' own "future work" sentence.
4. Sect. 3.1 reports real and substantial limitations: spatial-pattern correlations against ERA5 stay below 0.6 for all models for temperature and below 0.5 for all models for precipitation (L317-326); the multi-model mean smooths out the observed winter precipitation dipole (L264-268); only 7 of 11 models pass even the weak r > 0 screening criterion for precipitation (L330-336). These are acknowledged in the Discussion (L522-528, L580-592) but the Abstract (L10-25) reports the precipitation non-detection as a clean physical conclusion, with no indication that CMIP6's skill at reproducing the observed spatial structure of precipitation trends is itself a limiting factor. I'd suggest adding one sentence to the Abstrct, e.g.: "CMIP6 models show limited skill in reproducing the observed spatial structure of precipitation trends, which constrains confidence in the precipitation attribution results."
Minor comments