Anthropogenic Influence on Temperature and Precipitation Trends in the Western Mediterranean: A Multi-Method Approach

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Campos, Matías E. Olmo, Pep Cos, Margarida Samsó, Francisco Doblas-Reyes This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7904665/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The Western Mediterranean (WMed) is one of the regions most affected by climate change, where the interplay between anthropogenic forcings and strong natural variability produces complex spatial and seasonal patterns of change. This study analyses the anthropogenic imprint on 1951–2020 seasonal temperature and precipitation trends across climate-derived sub-regions of the WMed using multiple detection and attribution methods. A performance-based filtering of CMIP6 models was implemented to ensure an adequate representation of observed regional trends prior to attribution analyses. Detection and attribution were assessed using CMIP6 DAMIP single-forcing experiments through the Signal-to-Noise Ratio (SNR), the Fraction of Attributable Risk (FAR), and a statistical optimal fingerprinting method. Results reveal a robust anthropogenic imprint on temperature, with the amplitude of forced signals exceeding twice that of internal variability across all sub-regions. Greenhouse gas forcing emerges as the dominant driver of warming in both summer and winter, while anthropogenic aerosols exert a cooling effect that partially offsets greenhouse gas–induced warming. In contrast, precipitation trends remain within the bounds of internal variability, although detectable drying signals associated with greenhouse gas forcing appear over northern Africa and the southwestern Iberian Peninsula in winter. In summer, precipitation trends show contrasting responses to greenhouse gas and aerosol forcings. These findings highlight the value of regional-scale attribution frameworks and model performance filtering for reducing uncertainty in Mediterranean climate analyses, providing a basis for further attribution studies in this highly vulnerable region. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Environmental sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The Mediterranean Basin is recognised as a climate change hotspot due to its accelerated warming signal and high socio-ecological vulnerability (e.g., Cos et al., 2022 ; Tuel & Eltahir, 2020 ). Climate models project regional warming rates about 20% above the global average and a significant decline in precipitation—around 12% under a 3°C global warming scenario. These changes amplify existing hydroclimatic stresses in a region already prone to hot, dry summers (Ali et al., 2022 ). Additionally, more frequent and intense heatwaves, rising sea levels, and the potential increase in extreme rainfall events pose compounding risks to coastal infrastructure, biodiversity, and economic stability (Cramer et al., 2018 ; Driouech et al., 2020 ). Changes in regional temperature and precipitation have been documented on decadal to multidecadal scales. Radiative forcing from anthropogenic greenhouse gases (GHG) is generally addressed as the main external factor of warming (Feng et al., 2022 ; Stott, 2003 ; Urdiales-Flores et al., 2023 ; Van Oldenborgh et al., 2009 ). However, the accelerated warming since ~ 1980 has also been associated with the brightening effect caused by decreased anthropogenic aerosol emissions (Nabat et al., 2014 ; Philipona et al., 2009 ; Schumacher et al., 2024 ) or decreased soil moisture (Urdiales-Flores et al., 2023 ). The detection of precipitation changes over the region has proven more challenging than the detection of temperature changes. On the one hand, the sign and magnitude of trends vary depending on the period and season considered (Cherif et al., 2020 ; Vicente-Serrano et al., 2025 ). On the other hand, the strong decadal to interannual variability in the region (Campos et al., 2025 ; Mariotti et al., 2015 ; M. Olmo et al., 2024 ; Vicente-Serrano et al., 2025 ) hinders the robust detection of signals attributable to greenhouse gas emissions and other anthropogenic forcings over the historical period (Lionello et al., 2012 ; Peña-Angulo et al., 2020 ; Vicente-Serrano et al., 2020 ). At a sub-regional scale, the Mediterranean Basin exhibits substantial spatial and seasonal variability in both temperature and precipitation trends (Campos et al., 2025 ). During the 1951–2020 period, the most pronounced warming is observed over the Iberian Peninsula and northern Africa in summer, where the long-term trend accounts for more than 60% of the total temperature variance. Precipitation trends also show marked spatial variability. In summer, although not all sub-regions exhibit drying, significant negative trends are evident over the Iberian Peninsula and northern Africa. A similar pattern is found in winter, with notable drying concentrated in northern Africa and the southwestern Iberian Peninsula (Campos et al., 2025 ). Most attribution methods rely on climate model simulations (Allen & Stott, 2003 ; Hawkins & Sutton, 2012 ; Ribes et al., 2017 ; Ribes & Terray, 2013 ). According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC), the model ensemble from the Coupled Model Intercomparison Project Phase 6 (CMIP6) reproduces the global temperature trends with biases small enough to support detection and attribution studies (IPCC, 2022 ). However, at a regional scale, models may exhibit errors in the spatial and temporal patterns of the response to external forcings, or in the representation of internal variability, which can result in a mismatch between observed and simulated climate responses (Ribes & Terray, 2013 ). As in other applications—such as statistical downscaling or formulating regional projections—filtering model ensembles based on specific performance criteria (e.g., realistic representation of climate variability or long-term trends) has proven useful for controlling spread (McSweeney et al., 2015 ; Merrifield et al., 2023 ; M. E. Olmo et al., 2025 ; Palmer et al., 2023 ). By applying similar filtering approaches in attribution studies, the risk of misleading results could be minimised. This study aims to apply multiple detection and attribution methodologies to analyse long-term seasonal trends in temperature and precipitation across sub-regions of the Western Mediterranean (WMed; 10°W–25°E, 33°–45°N), using a unified methodological framework applied consistently across all seasons, variables, and regions. To this end, we use a set of CMIP6 models and their associated single-forcing experiments from the Detection and Attribution Model Intercomparison Project (DAMIP; Gillett et al., 2016 ). The remainder of the paper is organised as follows: Section 2 presents the results of the various detection and attribution approaches; Section 3 discusses the main findings in the context of previous literature; and Section 4 details the data and methodological framework employed in the analyses. 2. Results Observed and simulated temperature trends in the WMed are presented in Fig. 1 a–d. Both observations and models indicate stronger warming in summer than in winter; however, the model ensemble mean does not fully capture the spatial distribution of these trends (see Methods for a description of the dataset). For instance, in summer, the mean of observations shows more intense warming over the western WMed, particularly over the Iberian Peninsula and Northern Africa, whereas the ensemble mean depicts a more homogeneous warming pattern. Some individual models, however, may better reproduce the observed spatial features. For precipitation trends (Fig. 1 e–h), discrepancies between the ensemble mean and observations are more pronounced. Observations indicate a drying trend in winter, especially on the western side of the WMed, while the ensemble mean generally underestimates this trend and even simulates a wetting trend in the northern part of the domain. Again, while the ensemble mean smooths out trends, some models may still align more closely with observed patterns. In summer, observations show negative trends on the western side, particularly over the Iberian Peninsula, which are smoothed out in the ensemble mean. Additionally, positive trends observed over southern Italy, Greece, and parts of Northern Africa are poorly reproduced on average, although individual model performance may vary. To perform a robust detection and attribution analysis of the observed trends in the WMed, a model evaluation was conducted based on their ability to reproduce the spatial trend patterns. 2.1 Model evaluation This model evaluation process aims to select a subset of models that best reproduce the sign and spatial distribution of trends in the WMed region for detection and attribution analyses. Given that evaluating all the grid cells in the domain may be too demanding for models that are generally too coarse, the sub-regions defined by Campos et al. ( 2025 ) (Fig. 1 i) were used instead. The ensemble mean of each model was considered for this purpose (see the list of models and number of ensemble members in Table 1 and the description in Methods). The evaluation process for temperature trends is summarised in Fig. 2 . Models tend to underestimate regional differences in trend magnitude. Specifically, in sub-regions with strong summer positive observed trends, such as R4, R5, and R6, models underestimate the warming. In contrast, in sub-regions with weaker trends, like R2 and R3 in winter, they overestimate it (Fig. 2 b-c). As a result, correlations remain below 0.7 for all models, and the normalised standard deviation is below 1 for most models (Fig. 2 a). These discrepancies do not appear to arise from a misrepresentation of the sub-regional climatology, as models capture their distinct characteristics (Supplementary Fig. 1) and manage to reproduce the mean seasonal temperature (Supplementary Fig. 2). Precipitation trends are more diverse in magnitude and sign in the WMed compared to temperature trends, posing a greater challenge for models (Fig. 3 ). For example, despite the overall drying signal in winter, regions such as R5 and R7 show a stronger trend than R2 or R3. Likewise, in summer, regions like R2 and R7 display positive and negative trends of similar magnitude, respectively. The correlation between observed and simulated seasonal sub-regional trends does not exceed 0.4 for any model, and models tend to underestimate dispersion, with a normalised standard deviation below 0.7. As in the case of temperature, these discrepancies do not appear to derive from a misrepresentation of sub-regional climatologies, as models sufficiently capture mean precipitation values (Supplementary Fig. 3) and differences between sub-regions, despite some issues in winter (Supplementary Fig. 1). Given the models’ performance described above, particularly the limited representation of precipitation trends, a correlation threshold above zero (r > 0) in the spatial representation of the trends has been selected as the criterion for building the model subset for temperature and precipitation. Consequently, for temperature, the BCC-CSM2-MR model is excluded, while for precipitation, the selected models are MRI-ESM2-0, IPSL-CM6A-LR, MIROC6, HadGEM3-GC31-LL, and ACCESS-ESM1-5. The ensemble size is slightly reduced for temperature, whereas for precipitation, it is reduced by approximately half. Improvements in the representation of temperature trends remain limited when using the model subset (Fig. 2 b-c), whereas, for precipitation, they are more noticeable (Fig. 3 b-c); for instance, the mean trend from the model subset captures the negative winter precipitation trends in R4 and R6 more accurately. This aspect was missing when considering the full multi-model ensemble (Fig. 3 c). Likewise, in the summer, the subset represents the positive trends in R2 and R3 more effectively than the full ensemble (Fig. 3 c). Additionally, in some regions, the magnitude of precipitation trends is improved, such as in R5 and R7 during winter (Fig. 3 b). Despite these improvements, the model subset fails to capture the observed trends in certain regions—specifically, R2 for winter temperature, R1, R8, and R9 for winter precipitation, and R1 and R5 for summer precipitation. Therefore, attribution results in these regions should be interpreted cautiously and, in most cases, will be masked or excluded to prevent potential misinterpretations. 2.2 Detection and attribution of recent trends This section addresses the question of whether recent temperature and precipitation trends can be attributed to anthropogenic climate change using the Signal-to-Noise Ratio (SNR) approach (Hawkins & Sutton, 2012 ) and the Fraction of Attributable Risk (FAR) (Stott et al., 2016 ) (Methods). To this end, observed and simulated trends for the period 1951–2020, along with the ensemble members within the corresponding model subset, were considered. The SNR approach was applied to determine whether recent temperature and precipitation trends have emerged from internal variability. The SNR was calculated as the ratio of simulated trends (signal) to the standard deviation of trends from pre-industrial control runs (noise). Pre-industrial runs adequately capture temperature and precipitation variability, although they slightly underestimate winter precipitation variance in R4 and R7 (Supplementary Figs. 2 and 3). The Time of Emergence (ToE) is defined as the first year when the signal exceeds twice the noise (|SNR| >2) (Methods). Simulated temperature trends emerge from internal variability in both seasons across all sub-regions by the early 2000s (50-year-long trends). In winter, trends in sub-regions on the western side of the WMed (i.e., R7, R6, R5, and R4) emerge first, whereas during summer, there are fewer spatial differences (Fig. 4 a-b). In contrast, precipitation trends do not emerge from internal variability during the observed period, neither in winter nor in summer. However, R5 and R7 exhibit a prominent wintertime signal that, if the trend continues, may emerge in the near future (Fig. 4 c-d). The FAR was calculated as 1 - P NAT /P ALL , where P ALL and P NAT represent the probabilities of exceeding the given trend values (observed or simulated) in simulations with all forcings (using the Historical runs) and in simulations with only natural forcings (using the Nat runs), respectively (Methods and Supplementary Fig. 4). A FAR greater than 50% indicates that the probability of exceedance has more than doubled due to anthropogenic influence. FAR does not clearly exceed 50% for winter temperature trends, except in R5, where it exceeds 70%, and in R7, where it reaches 60%. In contrast, for summer temperature trends, FAR exceeds 95% in most regions (Fig. 5 a-b). The FAR values obtained using simulated trends (orange bars in Fig. 5 a-b) are generally consistent with those derived from observed trends (blue bars in Fig. 5 a-b). However, in regions where models overestimate trends (R1, R3, and R8 in winter, and R2 in summer), FAR values from models are substantially larger than the values obtained with observations, while in regions where models underestimate trends (R6 in winter), FAR values are notably lower. FAR for precipitation trends shows lower values and larger confidence intervals in both seasons compared to temperature trends (Fig. 5 c-d). Only the wintertime trends in R5 and R7 surpass 50%. Trends in R2 and R3 during winter, as well as in R2 and R7 during summer, fall within the 25–50% range (Fig. 5 c-d). In regions in which observed and simulated trends have strong discrepancies, the results are misleading; for instance, in R5, the simulated summer precipitation trend—opposite in sign to the observed, though non-significant, trend—produced a leftward shift of the Historical trend distribution relative to the Nat-only one (Supplementary Fig. 6), resulting in negative FAR values (Fig. 5 d). 2.3 The role of external factors To examine the role of greenhouse gases and anthropogenic aerosols on recent temperature and precipitation trends over the WMed, a statistical approach based on maximum likelihood estimators (Ribes et al., 2017 ) was applied to the Historical simulations together with the single-forced experiments (Methods). The temperature response to all forcings (ALL) exhibits a warming trend in both winter and summer, generally falling within the range of observational data (Fig. 6 a,b). This warming is more pronounced during summer. The GHG-only model simulations show a stronger warming trend than those driven by all forcings, with these differences being more marked in winter than in summer. In contrast, the AAer-only trends are negative in both seasons and particularly heterogeneous during summer, partially counteracting the GHG-induced warming. On the other hand, the response to natural forcings (Nat-only) does not appear to contribute significantly to the observed trends (Fig. 6 a,b). In both seasons, GHGs are the main external driver of the temperature trends, acting consistently in the warming direction; however, during winter, the contribution of other forcings cannot be entirely ruled out (Supplementary Table 1). In winter, the strongest GHG-induced warming occurs in R8 (+ 0.29°C/decade, 95% CI: 0.17–0.40), on the eastern side of the WMed, coinciding with the strongest AAer-induced cooling (–0.08°C/decade), although the latter is not statistically significant (95% CI: -0.16 to 0.007°C/decade). Overall, the AAer response in winter is relatively uniform across the WMed (Fig. 6 a). In contrast, during summer, the most intense GHG-induced warming is observed in R5 and R6, on the western side of the WMed, with mean values around + 0.35°C/decade. The AAer-induced cooling in summer is generally weak across the region and not statistically significant, although it is slightly stronger in R5, R6, and R7, all located in the southwestern part of the WMed (Fig. 6 b). The precipitation response to external forcings is less spatially uniform than the temperature response (Fig. 6 c,d). In winter, significant GHG-induced drying is observed in R2 and R3, located on the eastern side of the WMed, and is even more pronounced in the southwestern regions R5 and R7 (ranging from 2.5% to 3%/decade). In R7, although the drying signal is significant in both the observations (–6.4%/decade) and the GHG-induced response (–3%/decade, 95% CI: -4.3 to -1.6]), it is not statistically significant in the best estimate of ALL, which may reflect the large internal variability in that region (as suggested by the wide confidence interval in the Nat-only response). Both the AAer-only and Nat-only responses show no significant trends during winter (Fig. 6 c). In summer, significant GHG-induced drying is observed across most regions, partially compensated by positive trends in the AAer-induced responses (Fig. 6 d). In R6, for example, the negative GHG signal (–3.5%/decade, 95% CI:-6.7 to -0.5) is counteracted by a significant positive AAer response (+ 2.1%/decade, 95% CI: 0.5 to 3.7), resulting in an ALL response of − 1.5%/decade (95% CI: -5.1 to 2.1), which is notably weaker than the observed drying of − 4.6%/decade (Fig. 6 d). 3. Discussion The Western Mediterranean (WMed) stands out as one of the regions most affected by the ongoing climate change, where recent trends in temperature and precipitation reflect both global influences and complex regional processes (e.g., Campos et al., 2025 ; Cos et al., 2022 ; Olmo et al., 2025 ). Understanding the relative contributions of anthropogenic and natural factors is therefore essential to interpreting these changes and their implications for the region’s future climate. This study provides a comprehensive assessment of seasonal temperature and precipitation trends during 1951–2020 across climatic-derived sub-regions of the WMed, using multiple attribution methods and a performance-based selection of CMIP6 models and their associated single-forced experiments from the DAMIP to better identify the drivers of observed changes. Given that climate models often struggle to reliably reproduce the probability distribution of regional trends (Philip et al., 2020 ; Van Oldenborgh et al., 2013 ), an initial filtering step was applied before any attribution analysis. This filter was based on the spatial representation of observed trends following the regionalisation of Campos et al. ( 2025 ). Although this improved the representation of trends in most regions, especially for precipitation, discrepancies between observed and simulated trends still persisted in some cases (Figs. 2 and 3 ), warranting cautious interpretation of the attribution results, like for summer precipitation over northern Africa (see Fig. 5 d and Supplementary Fig. 6). As mentioned earlier, this study builds upon the application of multiple state-of-the-art attribution methodologies using the DAMIP experiments. By comparing pre-industrial (unforced) and historical (forced) variability, the Signal-to-Noise Ratio (SNR) of trends was computed. Similarly, the Fraction of Attributable Risk (FAR) was derived by contrasting the distribution of trends from Historical experiments (including both natural and anthropogenic forcings) with those from the experiment including only natural forcings ( Nat-only ). In addition, to enhance the robustness of the results obtained from single-forcing experiments—accounting for internal variability and model uncertainty—an alternative methodology to the traditional optimal fingerprints approach was applied (Ribes et al., 2017 ). Together, these analyses reveal a clear emergence of anthropogenic influence on temperature trends across the WMed, whereas precipitation trends remain within the bounds of internal variability. The SNR results show that temperature signals have exceeded twice the amplitude of natural variability (|SNR| >2) in all sub-regions, with an earlier emergence in the western part of the basin. In contrast, precipitation trends have not yet emerged from internal variability, although regions located in the northern and northwestern Africa (R5 and R7), exhibit signals that are approaching detectability in recent years. The emergence of a signal in precipitation trends over the WMed has been previously discussed in the literature and addressed from multiple perspectives. However, a robust emergence has not yet been detected in observations (Campos et al., 2025 ; Seager et al., 2024 ; Vicente-Serrano et al., 2025 ), despite consistent model projections indicating a drying signal emerging in the future (Giorgi & Bi, 2009 ; Mariotti et al., 2015 ; Seager et al., 2024 ). Temperature trends across the WMed are predominantly driven by anthropogenic forcings, as supported by the FAR analysis, which shows values exceeding 95% in most regions during summer —consistent with previous studies (e.g., Feng et al., 2022 ; Stott, 2003 ; Urdiales-Flores et al., 2023 ) (Fig. 5 a,b). Greenhouse gas (GHG) forcing emerges as the primary contributor to the observed warming in both seasons, as depicted in the analysis with the single-forced experiments (Fig. 6 a,b). In contrast, anthropogenic aerosol (AAer) forcing exerts a cooling influence that partially offsets the GHG-induced warming (Fig. 6 a,b). This cooling response during 1951–2020 is consistent with the so-called “global dimming” period (1950–1980; Wild, 2012 ), which has been documented over Europe and the Mediterranean (Nabat et al., 2014 ; Pfeifroth et al., 2018 ). However, since the early 1980s, a marked reduction in anthropogenic aerosol emissions (e.g., Floutsi et al., 2016 ; Li et al., 2014 ) has led to a “brightening effect” that has contributed to the accelerated warming observed over the WMed (Glantz et al., 2022 ; Nabat et al., 2014 ; Urdiales-Flores et al., 2023 ). The temperature response to AAer is not spatially uniform across the WMed. During 1951–2020, AAer-induced cooling exhibits a more homogeneous and stronger pattern in winter, whereas in summer it is more pronounced over the southwestern regions (R5, R6, and R7; Fig. 6 a,b). In contrast, during 1981–2020, the warming response becomes largely confined to summer, with the strongest signal over the eastern sub-regions (R1 and R8; Supplementary Fig. 5a,b). For precipitation, the different methods used consistently indicate a higher degree of uncertainty in the detected signals. FAR values are generally lower than for temperature, with exceedances above 50% confined to the southwestern WMed (R5 and R7) in winter. The response to external forcings is more heterogeneous and spatially complex than for temperature. In winter, the GHG-induced signal is associated with drying, particularly over the western sub-regions (R5, R6, and R7; Fig. 6 c), whereas in summer the drying is more spatially uniform and generally stronger. The AAer-only simulations show a weak response in winter, but a consistent wetting signal across all regions during summer, which counteracts the GHG-induced drying (Fig. 6 d). The dominance of GHG forcing in driving the winter drying signal is consistent with large-scale circulation changes associated with an increase in GHG, such as the poleward shift of the storm track (Previdi & Liepert, 2007 ) and increased subsidence over the region (Seager et al., 2014 ). During this season, the AAer signal, although weak and statistically insignificant, also exhibits a drying trend. Despite both forcings acting in the same direction, the strong internal variability in the region (Supplementary Fig. 3d) hinders the detection of these externally forced signals (Fig. 4 c; Campos et al., 2025 ; Peña-Angulo et al., 2020 ; Vicente-Serrano et al., 2020 ). In summer, the contrasting responses to GHG and AAer forcings—GHG-induced drying versus AAer-induced wetting—help explain the weaker and often statistically insignificant precipitation trends in the ALL-forcing estimates (Fig. 6 d). The wetting response associated with AAer may reflect aerosol–cloud interactions during a season dominated by convective precipitation (Christidis & Stott, 2022 ). These opposing effects add another layer of complexity, further hindering the robust detection of an anthropogenic signal in summer precipitation. Despite the reduction in AAer emissions during 1981–2020, as previously discussed, the summertime precipitation response remains positive in most sub-regions (Supplementary Fig. 5d). Overall, the results reveal a robust anthropogenic imprint on temperature trends across the WMed, mainly associated with GHG forcing, whereas precipitation changes remain uncertain and spatially variable, reflecting the combined effects of strong internal variability and compensating external forcings. This study focuses on regional-scale analyses to better capture the spatial heterogeneity of climatic responses across the WMed; nevertheless, this approach also entails certain limitations. The use of small sub-regions, while necessary to represent the WMed climatic heterogeneity, may enhance the relative contribution of internal variability, thereby complicating the detection of externally forced signals. This regional focus also poses challenges for global climate models, which still operate at relatively coarse spatial resolutions. Improving model resolution and optimising their representation of regional processes remain essential steps for advancing attribution studies at this scale. Nevertheless, in this study, no clear relationship was found between model resolution and the ability to reproduce observed trends (Figs. 2 and 3 ). Other aspects of sub-regional climate change in the WMed call for further investigation, particularly the behaviour of extreme events under continued global warming and their link to changes in atmospheric dynamics and physical processes. These aspects will be explored in future work to provide a more comprehensive understanding of the mechanisms driving regional climate responses. 4. Methods 4.1 Data and Model Evaluation For this study, monthly temperature and precipitation observational and simulated data have been used for the WMed region (10°W-25°E; 33°-45°N, see Fig. 1 i). For temperature, observations were taken from the ECMWF Reanalysis version 5 (ERA5, Hersbach et al., 2020 ), Berkeley Earth (Rohde & Hausfather, 2020 ) and the Climate Research Unit version TS4.07 (CRU; Harris et al., 2020 ), while for precipitation, ERA5, CRU and the Global Precipitation Climatology Centre version 2022 (GPCC; Schneider et al., 2022 ) databases were used. Monthly temperature and precipitation data from 11 CMIP6 models were obtained (Table 1 ). The Historical CMIP6 experiments span from 1850 to 2014 (Eyring et al., 2016 ); therefore, to complete the 1951–2020 period, the time series were extended using the SSP2-4.5 future scenario (Riahi et al., 2017 ). Additionally, a 500-year pre-industrial time series was obtained for each model from its piControl simulations. Historical single-forced simulation data from the DAMIP experiments (Gillett et al., 2016 ) were used. The experiments considered in this study are the well-mixed greenhouse-gas-only (GHG), anthropogenic-aerosol-only (AAer), and natural-only (Nat) scenarios. For the AAer runs, BC, OC, SO2, SO4, NOx, NH3, CO, and NMVOC are considered. Similarly, for the Nat experiments, solar irradiance and stratospheric aerosols are included. For details, the reader is referred to Gillett et al. ( 2016 ). The number of runs for each experiment and model is detailed in Table 1 . All data from the models were regridded to a common 1° x 1° grid using a linear interpolation method with the Earth System Model Evaluation Tool (ESMValTool; Righi et al., 2020 ). Table 1 CMIP6 models used in this study and number of members. Model Native resolution Number of ensemble members Reference Historical + SSP2-4.5 GHG-only AAer-only Nat-only ACCESS-CM2 1.25° x 1.875° 3 3 3 3 Ziehn et al. ( 2020 ) ACCESS-ESM1-5 1.25° x 1.875° 3 3 3 3 Ziehn et al. ( 2020 ) BCC-CSM2-MR 1.125° x 1.125° 3 3 3 3 Wu et al. ( 2021 ) CNRM-CM6-1 1.406° x 1.406° 10 10 10 10 Voldoire et al., ( 2019 ) CanESM5 2.812° x 2.812° 15 15 15 15 Swart et al. ( 2019 ) FGOALS-g3 2.25° x 2.0° 3 3 3 3 Li et al. ( 2020 ) HadGEM3-GC31-LL 1.25° x 1.875° 4 34 34 34 Andrews et al. ( 2020 ) IPSL-CM6A-LR 1.25° x 2.5° 6 10 10 10 Boucher et al. ( 2020 ) MIROC6 1.406° x 1.406° 10 10 10 10 Tatebe et al. ( 2019 ) MRI-ESM2-0 1.125° x 1.125° 5 5 5 5 Yukimoto et al. ( 2019 ) NorESM2-LM 1.25° x 3.75° 3 3 3 3 Bentsen et al. ( 2013 ) The WMed was divided into nine sub-regions based on the clustering by Campos et al. ( 2025 ) (Fig. 1 i). In that study, monthly temperature and precipitation data were reduced through Empirical Orthogonal Functions (EOFs), retaining 90% of the total variance, and then used as input data for a k-means clustering procedure. Several sensitivity analyses were performed, focusing on the number of clusters and the climatic homogeneity of the sub-regions obtained. For more details, the reader is referred to Campos et al. ( 2025 ). To assess whether both models and observational datasets can distinguish between these sub-regions, the Rank-Sum test (Mann & Whitney, 1947 ) was applied at a 5% significance level to the probability distribution functions of monthly temperature and precipitation, evaluated separately. Additionally, observed and simulated sub-regional means and variances were compared to further characterise the sub-regional differences. Since sub-regional trends summarise the spatial patterns of temperature and precipitation trends over the WMed, they can serve as a basis for evaluating the ability of models to reproduce these patterns. Taylor diagrams were constructed to quantify the statistical similarity between the observed mean reference and the different models, reporting the Pearson correlation coefficient, the normalised standard deviation, and the root mean squared error (Taylor, 2001 ). The analysis was based on n = 36 data points, corresponding to nine regions across four seasons, with the observed mean reference computed as the average of all observations. The ensemble mean of each model was used for this analysis. Based on the Taylor diagrams, a subset of models was selected for temperature and precipitation to be used in the subsequent detection and attribution analyses. 4.2 Detection and Attribution Methods To assess whether temperature and precipitation change signals have emerged from internal variability in recent decades, the Signal-to-Noise Ratio (SNR) approach (e.g., Chemke & Coumou, 2024 ) was applied. The signal was defined as the trend in temperature and precipitation from 1951 to each year up to 2020, while the noise was estimated by the standard deviation of all trends of the same length as signals derived from the pre-industrial control runs of each model. The SNR was obtained as the mean SNR value across all pre-industrial runs. Additionally, the Time of Emergence (ToE) approach (Hawkins & Sutton, 2012 ) was used to indicate when the trends emerge from internal variability. The ToE was defined as the year in which the signal exceeded 2 standard deviations of internal variability (|SNR| >2). To attribute recent trends in the WMed to anthropogenic emissions, the Fraction of Attributable Risk (FAR) approach (Stott et al., 2016 ) was applied to the sub-regional trends. Trends for the period 1951–2020 were calculated for all ensemble members from the temperature and precipitation model subsets, using both the Historical (all forcings) and Nat (natural-only forcings) experiments. For each set of trends, a Gaussian distribution was fitted. These distributions were then used to estimate the probability of exceedance for the observed trends (represented by the mean of observations) and the simulated trends (from the Historical multi-model ensemble mean) (see Supplementary Fig. 4 for an example). The probability of exceedance under all forcings is denoted as P ALL (from the Historical ensemble), and under natural-only forcings as P NAT (from the Nat ensemble). The FAR was calculated as 1 - P NAT / P ALL , representing the probability that a given trend is attributable to anthropogenic emissions. A FAR greater than 0.5 indicates that the probability of exceedance has more than doubled due to anthropogenic influence. 4.3 Contributions by external factors To investigate the influence of selected forcing factors on recent trends over the WMed (in this case, greenhouse gases, anthropogenic aerosols, and natural forcings), a statistical detection and attribution approach based on additive decomposition and maximum likelihood estimators was used. This approach was proposed by Ribes et al. ( 2017 ) as an alternative to the optimal fingerprints based on linear regression (e.g., Allen & Stott, 2003 ; Hegerl et al., 2011 ; Ribes et al., 2013 ) and has been applied in studies at a sub-regional scale (De Abreu et al., 2019 ). The method is based on the additivity assumption, in which \(\:{Y}^{*}\) represents the true observed climate response of the climate system to a number ( \(\:nf\) ) of true individual external forcings ( \(\:{X}_{i}^{*}\) ) taken together as: $$\:{Y}^{*}={\sum\:}_{i=1}^{nf}{X}_{i}^{*}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ $$\:Y\:=\:{Y}^{*}+\epsilon\:y,\:\:\:\:\epsilon\:y \sim N(0,\varSigma\:y)\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ $$\:{X}_{i}={X}_{i}^{*}+\epsilon\:x,\:\:\:\:\:\:\:\:\epsilon\:x\sim N(0,\varSigma\:{x}_{i}),\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:i=1,...,nf\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ where \(\:Y\) is the observation vector with \(\:\epsilon\:y\) observational uncertainty, and \(\:{X}_{i}\) is the simulated individual forcing vector with \(\:\epsilon\:x\) model uncertainty, with their respective covariance matrices \(\:\varSigma\:y\) and \(\:\varSigma\:{x}_{i}\) . It is assumed that the observational error arises from the internal variability, while the model error arises from the internal variability and model uncertainty. Both uncertainty distributions are assumed Gaussian, so \(\:{X}_{i}^{*}\) and \(\:{Y}^{*}\) can be estimated using Maximum Likelihood Estimators (MLE, \(\:{\widehat{Y}}^{*}\) and \(\:{\widehat{{X}_{i}}}^{*}\) ) as described in Ribes et al. ( 2017 ). As in De Abreu et al. ( 2019 ), the consistency with internal variability, as well as with different sets of external forcings, is assessed using a 𝝌2 test (Wilks, 2006 ). Trends are estimated using the best estimates \(\:{\widehat{Y}}^{*}\) and \(\:{\widehat{{X}_{i}}}^{*}\) . Confidence intervals are then constructed by computing trends from 4000 randomly generated vectors, from multivariate normal distributions using the mean and covariance matrices of \(\:{\widehat{Y}}^{*}\) and \(\:{\widehat{{X}_{i}}}^{*}\) , respectively. In this study, instead of being the observation vector, \(\:Y\) was derived from the Historical multi-model ensemble mean of the corresponding model subset for temperature and precipitation, following the methodology described above. The simulated individual forcing vectors \(\:{X}_{i}^{}\) were obtained from the DAMIP experiments, using the same model subset. The number of ensemble members per model is provided in Table 1 . Similarly, the internal variability matrix was obtained from the pre-industrial control runs. The vectors \(\:Y,{X}_{i}^{}\:Z\) were then obtained using the decadal averages for each season and sub-region in the WMed region. A similar exercise using the observations to derive \(\:Y\) was performed, yielding similar results (not shown). The analysis was performed using the Python package described in De Abreu et al. ( 2019 ), which is publicly available on GitHub ( https://github.com/rafaelcabreu/attribution/ ). Declarations 7. Author Contribution Statement DC, MO, and FDR designed the study. DC developed the diagnostics and analyses and wrote the initial manuscript. MS downloaded and organised the data. DC, MO, PC, and FDR contributed to the interpretation and discussion of the results and the improvement of the manuscript. 5. Funding Statement This work was supported by the CLIMCAT project: “Plan for comprehensive climate change information for Catalonia". MO is funded by the AI4Science PN070500 fellowship within the “Generación D” initiative, Red.es, Ministerio para la Transformación Digital y de la Función Pública, for talent attraction (C005/24-ED CV1). Funded by the European Union NextGenerationEU funds, through PRTR. Author Contribution DC, MO, and FDR designed the study. DC developed the diagnostics and analyses and wrote the initial manuscript. MS downloaded and organised the data. DC, MO, PC, and FDR contributed to the interpretation and discussion of the results and the improvement of the manuscript. Acknowledgement This study was funded by the CLIMCAT project: “Plan for comprehensive climate change information for Catalonia". Data Availability Monthly ERA5 data is available from the Copernicus Climate Change Service (Copernicus Climate Change Service, 2019). CRU monthly temperature and precipitation data are available from the Climatic Research Unit webpage ( [https://crudata.uea.ac.uk/cru/data/hrg/#info](https:/crudata.uea.ac.uk/cru/data/hrg) ), Berkeley Earth monthly temperature is available from the Berkeley Earth data webpage ( [https://berkeleyearth.org/data/](https:/berkeleyearth.org/data) ), GPCC monthly precipitation data is available from the DWD webpage [(https://www.dwd.de/EN/ourservices/gpcc/gpcc.html](https:/www.dwd.de/EN/ourservices/gpcc/gpcc.html) ). 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Nature . https://doi.org/10.1038/s41586-024-08576-6 Voldoire, A., Saint-Martin, D., Sénési, S., Decharme, B., Alias, A., Chevallier, M., Colin, J., Guérémy, J. ‐F., Michou, M., Moine, M. ‐P., Nabat, P., Roehrig, R., Salas Y Mélia, D., Séférian, R., Valcke, S., Beau, I., Belamari, S., Berthet, S., Cassou, C., … Waldman, R. (2019). Evaluation of CMIP6 DECK Experiments With CNRM‐CM6‐1. Journal of Advances in Modeling Earth Systems , 11 (7), 2177–2213. https://doi.org/10.1029/2019MS001683 Wild, M. (2012). Enlightening Global Dimming and Brightening. Bulletin of the American Meteorological Society , 93 (1), 27–37. https://doi.org/10.1175/BAMS-D-11-00074.1 Wilks, D. S. (2006). Statistical methods in the atmospheric sciences (2nd ed). Academic Press. Wu, T., Yu, R., Lu, Y., Jie, W., Fang, Y., Zhang, J., Zhang, L., Xin, X., Li, L., Wang, Z., Liu, Y., Zhang, F., Wu, F., Chu, M., Li, J., Li, W., Zhang, Y., Shi, X., Zhou, W., … Hu, A. (2021). BCC-CSM2-HR: A high-resolution version of the Beijing Climate Center Climate System Model. Geoscientific Model Development , 14 (5), 2977–3006. https://doi.org/10.5194/gmd-14-2977-2021 Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S., Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H., Shindo, E., Mizuta, R., Obata, A., Adachi, Y., & Ishii, M. (2019). The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: Description and Basic Evaluation of the Physical Component. Journal of the Meteorological Society of Japan. Ser. II , 97 (5), 931–965. https://doi.org/10.2151/jmsj.2019-051 Ziehn, T., Chamberlain, M. A., Law, R. M., Lenton, A., Bodman, R. W., Dix, M., Stevens, L., Wang, Y.-P., & Srbinovsky, J. (2020). The Australian Earth System Model: ACCESS-ESM1.5. Journal of Southern Hemisphere Earth Systems Science , 70 (1), 193–214. https://doi.org/10.1071/ES19035 Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformationnpj.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7904665","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":540413964,"identity":"f6ff2efc-923e-4cfe-b2c7-ba4c4ae3ecaa","order_by":0,"name":"Diego A. Campos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYDCCw8icjw2kamGcSZSWA0hsZl5itPAdZz784gODnbzu7ObDn2131MkzsLc/wKtF8jBbmuUMhmTDbXeOpUnnnjls2MBzxgCvFoPDPGbGPAwHGLfdyDFjzm07kMAgkYPfYQaH+b+BtNgDtRh/tmyrS2CQf47fYUBbmB8DtSQCtRhIM7YxA21hwO8woF/MGGcYJCeD/CLZC/RLG08Ofi185w8//vChws522+3mwx9+AkOMn/04focBARvEJRIwLiH1QMD8AUxJEFA2CkbBKBgFIxcAAAiaR2eYts/QAAAAAElFTkSuQmCC","orcid":"","institution":"Barcelona Supercomputing Center","correspondingAuthor":true,"prefix":"","firstName":"Diego","middleName":"A.","lastName":"Campos","suffix":""},{"id":540413965,"identity":"963c58c8-3b48-4c11-926c-de8123334d18","order_by":1,"name":"Matías E. 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1","display":"","copyAsset":false,"role":"figure","size":2811569,"visible":true,"origin":"","legend":"\u003cp\u003eObserved (mean of observations) and simulated (model ensemble mean) trends of temperature (a-d) and precipitation (e-h) in the Western Mediterranean in the 1951-2020 period for \u003cstrong\u003e(a)\u003c/strong\u003e winter temperature in observations, \u003cstrong\u003e(b)\u003c/strong\u003e winter temperature in models, \u003cstrong\u003e(c)\u003c/strong\u003e summer temperature in observations, \u003cstrong\u003e(d)\u003c/strong\u003e summer temperature in models, \u003cstrong\u003e(e)\u003c/strong\u003e winter precipitation in observations, \u003cstrong\u003e(f)\u003c/strong\u003e winter precipitation in models, \u003cstrong\u003e(g)\u003c/strong\u003e summer precipitation in observations, and \u003cstrong\u003e(h)\u003c/strong\u003e summer precipitation in models. \u003cstrong\u003e(i)\u003c/strong\u003e Clustering of the Western Mediterranean taken from (Campos et al., 2025).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/a6ca96acefab2aa77b383a54.png"},{"id":95386185,"identity":"290af006-0003-453a-89d3-566a99c3cf61","added_by":"auto","created_at":"2025-11-07 12:56:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":596569,"visible":true,"origin":"","legend":"\u003cp\u003eModel evaluation for temperature trends using the ensemble mean of each model and the mean of observations as reference. \u003cstrong\u003e(a)\u003c/strong\u003e Taylor diagram based on the seasonal sub-regional trends. The black star indicates the mean of the observations as a reference, while triangles represent the observations and circles represent the models. \u003cstrong\u003e(b)\u003c/strong\u003eSub-regional winter temperature trends from observations (blue), all models (green) and the model subset (orange). Colored bars show the mean trend for each case, while vertical lines indicate the range: minimum to maximum for observations, and the 10th–90th percentile interval for model ensembles. \u003cstrong\u003e(c)\u003c/strong\u003e As in (b), but for summer temperature.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/c71733383db643b0e0be31a4.png"},{"id":95386190,"identity":"45056542-e079-48d6-900e-8d03068286a6","added_by":"auto","created_at":"2025-11-07 12:56:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":583940,"visible":true,"origin":"","legend":"\u003cp\u003eAs in Figure 2, but for precipitation trends.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/429a3b3760f98e404fe704db.png"},{"id":95525533,"identity":"31e0d313-ef68-4f62-8348-4f5043f12000","added_by":"auto","created_at":"2025-11-10 10:05:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":567550,"visible":true,"origin":"","legend":"\u003cp\u003eSignal-to-Noise Ratio (SNR) analysis of the \u003cstrong\u003e(a)\u003c/strong\u003e winter temperature trends, \u003cstrong\u003e(b)\u003c/strong\u003e summer temperature trends, \u003cstrong\u003e(c)\u003c/strong\u003e winter precipitation trends, and \u003cstrong\u003e(d)\u003c/strong\u003e summer precipitation trends in the Western Mediterranean sub-regions (colours, legend in panel (b)). Shadings indicate the 95% confidence interval of the SNR for all sub-regions. The horizontal dashed lines mark the SNR of -2 and 2. The evolution has been smoothed with a 5-year running mean for plotting purposes. Omitted regions in each season correspond to those where observed and simulated trends show the strongest discrepancies.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/805a1651a1f89224d8219301.png"},{"id":95526780,"identity":"f51fe4b1-8db1-4554-8436-87d124f0ed9c","added_by":"auto","created_at":"2025-11-10 10:07:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":384309,"visible":true,"origin":"","legend":"\u003cp\u003eFraction of Attributable Risk (FAR) of observed (blue) and simulated (orange) trends in the WMed regions (from R1 to R9) for \u003cstrong\u003e(a)\u003c/strong\u003e winter temperature, \u003cstrong\u003e(b)\u003c/strong\u003e summer temperature, \u003cstrong\u003e(c)\u003c/strong\u003e winter precipitation, and \u003cstrong\u003e(d)\u003c/strong\u003e summer precipitation. Vertical lines represent the 95% confidence interval. Masked regions correspond to those where observed and simulated trends show the strongest discrepancies.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/c335344249fb133b18f5cd2b.png"},{"id":95386197,"identity":"5fff92d6-19d0-45ca-b628-ee42bb45cfd0","added_by":"auto","created_at":"2025-11-07 12:56:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":293821,"visible":true,"origin":"","legend":"\u003cp\u003e1951-2020 trends in forced model simulations and observations on the sub-regions of the Western Mediterranean using the corresponding model subset. \u003cstrong\u003e(a)\u003c/strong\u003eWinter temperature, \u003cstrong\u003e(b)\u003c/strong\u003e Summer temperature, \u003cstrong\u003e(c)\u003c/strong\u003e Winter precipitation, and \u003cstrong\u003e(d)\u003c/strong\u003e Summer precipitation. Simulations with all forcings (ALL, in turquoise), greenhouse gas forcing only (GHG, in pink), anthropogenic aerosols only (AAer, in purple), and natural only (Nat, in green). Bars show the trend from the signal model's best estimates (\u003cem\u003eŶ*\u003c/em\u003e for ALL and X\u003csup\u003e^*\u003c/sup\u003e\u003csub\u003el\u003c/sub\u003efor the single-forced experiments), and error bars show the 95% confidence interval. Observed trends are presented with star markers. Masked regions correspond to those where observed and simulated trends show the strongest discrepancies.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/c9a87ab308cd7cff92a52e72.png"},{"id":100356109,"identity":"b1aac62d-9f85-40b9-9baa-8853aa75d7af","added_by":"auto","created_at":"2026-01-16 06:52:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5830101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/e072b835-66d5-48df-95f7-aa260c18be94.pdf"},{"id":95386184,"identity":"11a4195f-32df-4cb4-8b0f-1a6284dccb94","added_by":"auto","created_at":"2025-11-07 12:56:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1673775,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationnpj.docx","url":"https://assets-eu.researchsquare.com/files/rs-7904665/v1/60d69ac42175af7ed3091d8c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Anthropogenic Influence on Temperature and Precipitation Trends in the Western Mediterranean: A Multi-Method Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Mediterranean Basin is recognised as a climate change hotspot due to its accelerated warming signal and high socio-ecological vulnerability (e.g., Cos et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tuel \u0026amp; Eltahir, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Climate models project regional warming rates about 20% above the global average and a significant decline in precipitation\u0026mdash;around 12% under a 3\u0026deg;C global warming scenario. These changes amplify existing hydroclimatic stresses in a region already prone to hot, dry summers (Ali et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, more frequent and intense heatwaves, rising sea levels, and the potential increase in extreme rainfall events pose compounding risks to coastal infrastructure, biodiversity, and economic stability (Cramer et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Driouech et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eChanges in regional temperature and precipitation have been documented on decadal to multidecadal scales. Radiative forcing from anthropogenic greenhouse gases (GHG) is generally addressed as the main external factor of warming (Feng et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stott, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Urdiales-Flores et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Van Oldenborgh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, the accelerated warming since ~\u0026thinsp;1980 has also been associated with the brightening effect caused by decreased anthropogenic aerosol emissions (Nabat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Philipona et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Schumacher et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) or decreased soil moisture (Urdiales-Flores et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe detection of precipitation changes over the region has proven more challenging than the detection of temperature changes. On the one hand, the sign and magnitude of trends vary depending on the period and season considered (Cherif et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, the strong decadal to interannual variability in the region (Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mariotti et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; M. Olmo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) hinders the robust detection of signals attributable to greenhouse gas emissions and other anthropogenic forcings over the historical period (Lionello et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pe\u0026ntilde;a-Angulo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt a sub-regional scale, the Mediterranean Basin exhibits substantial spatial and seasonal variability in both temperature and precipitation trends (Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). During the 1951\u0026ndash;2020 period, the most pronounced warming is observed over the Iberian Peninsula and northern Africa in summer, where the long-term trend accounts for more than 60% of the total temperature variance. Precipitation trends also show marked spatial variability. In summer, although not all sub-regions exhibit drying, significant negative trends are evident over the Iberian Peninsula and northern Africa. A similar pattern is found in winter, with notable drying concentrated in northern Africa and the southwestern Iberian Peninsula (Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMost attribution methods rely on climate model simulations (Allen \u0026amp; Stott, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hawkins \u0026amp; Sutton, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ribes et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ribes \u0026amp; Terray, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (AR6, IPCC), the model ensemble from the Coupled Model Intercomparison Project Phase 6 (CMIP6) reproduces the global temperature trends with biases small enough to support detection and attribution studies (IPCC, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, at a regional scale, models may exhibit errors in the spatial and temporal patterns of the response to external forcings, or in the representation of internal variability, which can result in a mismatch between observed and simulated climate responses (Ribes \u0026amp; Terray, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). As in other applications\u0026mdash;such as statistical downscaling or formulating regional projections\u0026mdash;filtering model ensembles based on specific performance criteria (e.g., realistic representation of climate variability or long-term trends) has proven useful for controlling spread (McSweeney et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Merrifield et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; M. E. Olmo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Palmer et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By applying similar filtering approaches in attribution studies, the risk of misleading results could be minimised.\u003c/p\u003e\u003cp\u003eThis study aims to apply multiple detection and attribution methodologies to analyse long-term seasonal trends in temperature and precipitation across sub-regions of the Western Mediterranean (WMed; 10\u0026deg;W\u0026ndash;25\u0026deg;E, 33\u0026deg;\u0026ndash;45\u0026deg;N), using a unified methodological framework applied consistently across all seasons, variables, and regions. To this end, we use a set of CMIP6 models and their associated single-forcing experiments from the Detection and Attribution Model Intercomparison Project (DAMIP; Gillett et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The remainder of the paper is organised as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the results of the various detection and attribution approaches; Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e discusses the main findings in the context of previous literature; and Section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e4\u003c/span\u003e details the data and methodological framework employed in the analyses.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003eObserved and simulated temperature trends in the WMed are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;d. Both observations and models indicate stronger warming in summer than in winter; however, the model ensemble mean does not fully capture the spatial distribution of these trends (see Methods for a description of the dataset). For instance, in summer, the mean of observations shows more intense warming over the western WMed, particularly over the Iberian Peninsula and Northern Africa, whereas the ensemble mean depicts a more homogeneous warming pattern. Some individual models, however, may better reproduce the observed spatial features. For precipitation trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee\u0026ndash;h), discrepancies between the ensemble mean and observations are more pronounced. Observations indicate a drying trend in winter, especially on the western side of the WMed, while the ensemble mean generally underestimates this trend and even simulates a wetting trend in the northern part of the domain. Again, while the ensemble mean smooths out trends, some models may still align more closely with observed patterns. In summer, observations show negative trends on the western side, particularly over the Iberian Peninsula, which are smoothed out in the ensemble mean. Additionally, positive trends observed over southern Italy, Greece, and parts of Northern Africa are poorly reproduced on average, although individual model performance may vary.\u003c/p\u003e\u003cp\u003eTo perform a robust detection and attribution analysis of the observed trends in the WMed, a model evaluation was conducted based on their ability to reproduce the spatial trend patterns.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Model evaluation\u003c/h2\u003e\u003cp\u003eThis model evaluation process aims to select a subset of models that best reproduce the sign and spatial distribution of trends in the WMed region for detection and attribution analyses. Given that evaluating all the grid cells in the domain may be too demanding for models that are generally too coarse, the sub-regions defined by Campos et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei) were used instead. The ensemble mean of each model was considered for this purpose (see the list of models and number of ensemble members in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and the description in Methods).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe evaluation process for temperature trends is summarised in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Models tend to underestimate regional differences in trend magnitude. Specifically, in sub-regions with strong summer positive observed trends, such as R4, R5, and R6, models underestimate the warming. In contrast, in sub-regions with weaker trends, like R2 and R3 in winter, they overestimate it (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c). As a result, correlations remain below 0.7 for all models, and the normalised standard deviation is below 1 for most models (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). These discrepancies do not appear to arise from a misrepresentation of the sub-regional climatology, as models capture their distinct characteristics (Supplementary Fig.\u0026nbsp;1) and manage to reproduce the mean seasonal temperature (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e\u003cp\u003ePrecipitation trends are more diverse in magnitude and sign in the WMed compared to temperature trends, posing a greater challenge for models (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For example, despite the overall drying signal in winter, regions such as R5 and R7 show a stronger trend than R2 or R3. Likewise, in summer, regions like R2 and R7 display positive and negative trends of similar magnitude, respectively. The correlation between observed and simulated seasonal sub-regional trends does not exceed 0.4 for any model, and models tend to underestimate dispersion, with a normalised standard deviation below 0.7. As in the case of temperature, these discrepancies do not appear to derive from a misrepresentation of sub-regional climatologies, as models sufficiently capture mean precipitation values (Supplementary Fig.\u0026nbsp;3) and differences between sub-regions, despite some issues in winter (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGiven the models\u0026rsquo; performance described above, particularly the limited representation of precipitation trends, a correlation threshold above zero (r\u0026thinsp;\u0026gt;\u0026thinsp;0) in the spatial representation of the trends has been selected as the criterion for building the model subset for temperature and precipitation. Consequently, for temperature, the BCC-CSM2-MR model is excluded, while for precipitation, the selected models are MRI-ESM2-0, IPSL-CM6A-LR, MIROC6, HadGEM3-GC31-LL, and ACCESS-ESM1-5. The ensemble size is slightly reduced for temperature, whereas for precipitation, it is reduced by approximately half.\u003c/p\u003e\u003cp\u003eImprovements in the representation of temperature trends remain limited when using the model subset (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb-c), whereas, for precipitation, they are more noticeable (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c); for instance, the mean trend from the model subset captures the negative winter precipitation trends in R4 and R6 more accurately. This aspect was missing when considering the full multi-model ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Likewise, in the summer, the subset represents the positive trends in R2 and R3 more effectively than the full ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Additionally, in some regions, the magnitude of precipitation trends is improved, such as in R5 and R7 during winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Despite these improvements, the model subset fails to capture the observed trends in certain regions\u0026mdash;specifically, R2 for winter temperature, R1, R8, and R9 for winter precipitation, and R1 and R5 for summer precipitation. Therefore, attribution results in these regions should be interpreted cautiously and, in most cases, will be masked or excluded to prevent potential misinterpretations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Detection and attribution of recent trends\u003c/h2\u003e\u003cp\u003eThis section addresses the question of whether recent temperature and precipitation trends can be attributed to anthropogenic climate change using the Signal-to-Noise Ratio (SNR) approach (Hawkins \u0026amp; Sutton, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the Fraction of Attributable Risk (FAR) (Stott et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Methods). To this end, observed and simulated trends for the period 1951\u0026ndash;2020, along with the ensemble members within the corresponding model subset, were considered.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe SNR approach was applied to determine whether recent temperature and precipitation trends have emerged from internal variability. The SNR was calculated as the ratio of simulated trends (signal) to the standard deviation of trends from pre-industrial control runs (noise). Pre-industrial runs adequately capture temperature and precipitation variability, although they slightly underestimate winter precipitation variance in R4 and R7 (Supplementary Figs.\u0026nbsp;2 and 3).\u003c/p\u003e\u003cp\u003eThe Time of Emergence (ToE) is defined as the first year when the signal exceeds twice the noise (|SNR| \u0026gt;2) (Methods). Simulated temperature trends emerge from internal variability in both seasons across all sub-regions by the early 2000s (50-year-long trends). In winter, trends in sub-regions on the western side of the WMed (i.e., R7, R6, R5, and R4) emerge first, whereas during summer, there are fewer spatial differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-b). In contrast, precipitation trends do not emerge from internal variability during the observed period, neither in winter nor in summer. However, R5 and R7 exhibit a prominent wintertime signal that, if the trend continues, may emerge in the near future (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec-d).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe FAR was calculated as 1 - P\u003csub\u003eNAT\u003c/sub\u003e/P\u003csub\u003eALL\u003c/sub\u003e, where P\u003csub\u003eALL\u003c/sub\u003e and P\u003csub\u003eNAT\u003c/sub\u003e represent the probabilities of exceeding the given trend values (observed or simulated) in simulations with all forcings (using the \u003cem\u003eHistorical\u003c/em\u003e runs) and in simulations with only natural forcings (using the \u003cem\u003eNat\u003c/em\u003e runs), respectively (Methods and Supplementary Fig.\u0026nbsp;4). A FAR greater than 50% indicates that the probability of exceedance has more than doubled due to anthropogenic influence. FAR does not clearly exceed 50% for winter temperature trends, except in R5, where it exceeds 70%, and in R7, where it reaches 60%. In contrast, for summer temperature trends, FAR exceeds 95% in most regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b). The FAR values obtained using simulated trends (orange bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b) are generally consistent with those derived from observed trends (blue bars in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b). However, in regions where models overestimate trends (R1, R3, and R8 in winter, and R2 in summer), FAR values from models are substantially larger than the values obtained with observations, while in regions where models underestimate trends (R6 in winter), FAR values are notably lower. FAR for precipitation trends shows lower values and larger confidence intervals in both seasons compared to temperature trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). Only the wintertime trends in R5 and R7 surpass 50%. Trends in R2 and R3 during winter, as well as in R2 and R7 during summer, fall within the 25\u0026ndash;50% range (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). In regions in which observed and simulated trends have strong discrepancies, the results are misleading; for instance, in R5, the simulated summer precipitation trend\u0026mdash;opposite in sign to the observed, though non-significant, trend\u0026mdash;produced a leftward shift of the \u003cem\u003eHistorical\u003c/em\u003e trend distribution relative to the \u003cem\u003eNat-only\u003c/em\u003e one (Supplementary Fig.\u0026nbsp;6), resulting in negative FAR values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The role of external factors\u003c/h2\u003e\u003cp\u003eTo examine the role of greenhouse gases and anthropogenic aerosols on recent temperature and precipitation trends over the WMed, a statistical approach based on maximum likelihood estimators (Ribes et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was applied to the \u003cem\u003eHistorical\u003c/em\u003e simulations together with the single-forced experiments (Methods).\u003c/p\u003e\u003cp\u003eThe temperature response to all forcings (ALL) exhibits a warming trend in both winter and summer, generally falling within the range of observational data (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). This warming is more pronounced during summer. The GHG-only model simulations show a stronger warming trend than those driven by all forcings, with these differences being more marked in winter than in summer. In contrast, the AAer-only trends are negative in both seasons and particularly heterogeneous during summer, partially counteracting the GHG-induced warming. On the other hand, the response to natural forcings (Nat-only) does not appear to contribute significantly to the observed trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). In both seasons, GHGs are the main external driver of the temperature trends, acting consistently in the warming direction; however, during winter, the contribution of other forcings cannot be entirely ruled out (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn winter, the strongest GHG-induced warming occurs in R8 (+\u0026thinsp;0.29\u0026deg;C/decade, 95% CI: 0.17\u0026ndash;0.40), on the eastern side of the WMed, coinciding with the strongest AAer-induced cooling (\u0026ndash;0.08\u0026deg;C/decade), although the latter is not statistically significant (95% CI: -0.16 to 0.007\u0026deg;C/decade). Overall, the AAer response in winter is relatively uniform across the WMed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In contrast, during summer, the most intense GHG-induced warming is observed in R5 and R6, on the western side of the WMed, with mean values around +\u0026thinsp;0.35\u0026deg;C/decade. The AAer-induced cooling in summer is generally weak across the region and not statistically significant, although it is slightly stronger in R5, R6, and R7, all located in the southwestern part of the WMed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe precipitation response to external forcings is less spatially uniform than the temperature response (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec,d). In winter, significant GHG-induced drying is observed in R2 and R3, located on the eastern side of the WMed, and is even more pronounced in the southwestern regions R5 and R7 (ranging from 2.5% to 3%/decade). In R7, although the drying signal is significant in both the observations (\u0026ndash;6.4%/decade) and the GHG-induced response (\u0026ndash;3%/decade, 95% CI: -4.3 to -1.6]), it is not statistically significant in the best estimate of ALL, which may reflect the large internal variability in that region (as suggested by the wide confidence interval in the Nat-only response). Both the AAer-only and Nat-only responses show no significant trends during winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003eIn summer, significant GHG-induced drying is observed across most regions, partially compensated by positive trends in the AAer-induced responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). In R6, for example, the negative GHG signal (\u0026ndash;3.5%/decade, 95% CI:-6.7 to -0.5) is counteracted by a significant positive AAer response (+\u0026thinsp;2.1%/decade, 95% CI: 0.5 to 3.7), resulting in an ALL response of \u0026minus;\u0026thinsp;1.5%/decade (95% CI: -5.1 to 2.1), which is notably weaker than the observed drying of \u0026minus;\u0026thinsp;4.6%/decade (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThe Western Mediterranean (WMed) stands out as one of the regions most affected by the ongoing climate change, where recent trends in temperature and precipitation reflect both global influences and complex regional processes (e.g., Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Cos et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Olmo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding the relative contributions of anthropogenic and natural factors is therefore essential to interpreting these changes and their implications for the region\u0026rsquo;s future climate. This study provides a comprehensive assessment of seasonal temperature and precipitation trends during 1951\u0026ndash;2020 across climatic-derived sub-regions of the WMed, using multiple attribution methods and a performance-based selection of CMIP6 models and their associated single-forced experiments from the DAMIP to better identify the drivers of observed changes.\u003c/p\u003e\u003cp\u003eGiven that climate models often struggle to reliably reproduce the probability distribution of regional trends (Philip et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Van Oldenborgh et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), an initial filtering step was applied before any attribution analysis. This filter was based on the spatial representation of observed trends following the regionalisation of Campos et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although this improved the representation of trends in most regions, especially for precipitation, discrepancies between observed and simulated trends still persisted in some cases (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), warranting cautious interpretation of the attribution results, like for summer precipitation over northern Africa (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed and Supplementary Fig.\u0026nbsp;6).\u003c/p\u003e\u003cp\u003eAs mentioned earlier, this study builds upon the application of multiple state-of-the-art attribution methodologies using the DAMIP experiments. By comparing pre-industrial (unforced) and historical (forced) variability, the Signal-to-Noise Ratio (SNR) of trends was computed. Similarly, the Fraction of Attributable Risk (FAR) was derived by contrasting the distribution of trends from \u003cem\u003eHistorical\u003c/em\u003e experiments (including both natural and anthropogenic forcings) with those from the experiment including only natural forcings (\u003cem\u003eNat-only\u003c/em\u003e). In addition, to enhance the robustness of the results obtained from single-forcing experiments\u0026mdash;accounting for internal variability and model uncertainty\u0026mdash;an alternative methodology to the traditional optimal fingerprints approach was applied (Ribes et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Together, these analyses reveal a clear emergence of anthropogenic influence on temperature trends across the WMed, whereas precipitation trends remain within the bounds of internal variability.\u003c/p\u003e\u003cp\u003eThe SNR results show that temperature signals have exceeded twice the amplitude of natural variability (|SNR| \u0026gt;2) in all sub-regions, with an earlier emergence in the western part of the basin. In contrast, precipitation trends have not yet emerged from internal variability, although regions located in the northern and northwestern Africa (R5 and R7), exhibit signals that are approaching detectability in recent years. The emergence of a signal in precipitation trends over the WMed has been previously discussed in the literature and addressed from multiple perspectives. However, a robust emergence has not yet been detected in observations (Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Seager et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), despite consistent model projections indicating a drying signal emerging in the future (Giorgi \u0026amp; Bi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mariotti et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Seager et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemperature trends across the WMed are predominantly driven by anthropogenic forcings, as supported by the FAR analysis, which shows values exceeding 95% in most regions during summer \u0026mdash;consistent with previous studies (e.g., Feng et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stott, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Urdiales-Flores et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea,b). Greenhouse gas (GHG) forcing emerges as the primary contributor to the observed warming in both seasons, as depicted in the analysis with the single-forced experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). In contrast, anthropogenic aerosol (AAer) forcing exerts a cooling influence that partially offsets the GHG-induced warming (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). This cooling response during 1951\u0026ndash;2020 is consistent with the so-called \u0026ldquo;global dimming\u0026rdquo; period (1950\u0026ndash;1980; Wild, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), which has been documented over Europe and the Mediterranean (Nabat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pfeifroth et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, since the early 1980s, a marked reduction in anthropogenic aerosol emissions (e.g., Floutsi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) has led to a \u0026ldquo;brightening effect\u0026rdquo; that has contributed to the accelerated warming observed over the WMed (Glantz et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nabat et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Urdiales-Flores et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The temperature response to AAer is not spatially uniform across the WMed. During 1951\u0026ndash;2020, AAer-induced cooling exhibits a more homogeneous and stronger pattern in winter, whereas in summer it is more pronounced over the southwestern regions (R5, R6, and R7; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea,b). In contrast, during 1981\u0026ndash;2020, the warming response becomes largely confined to summer, with the strongest signal over the eastern sub-regions (R1 and R8; Supplementary Fig.\u0026nbsp;5a,b).\u003c/p\u003e\u003cp\u003eFor precipitation, the different methods used consistently indicate a higher degree of uncertainty in the detected signals. FAR values are generally lower than for temperature, with exceedances above 50% confined to the southwestern WMed (R5 and R7) in winter. The response to external forcings is more heterogeneous and spatially complex than for temperature. In winter, the GHG-induced signal is associated with drying, particularly over the western sub-regions (R5, R6, and R7; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec), whereas in summer the drying is more spatially uniform and generally stronger. The AAer-only simulations show a weak response in winter, but a consistent wetting signal across all regions during summer, which counteracts the GHG-induced drying (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eThe dominance of GHG forcing in driving the winter drying signal is consistent with large-scale circulation changes associated with an increase in GHG, such as the poleward shift of the storm track (Previdi \u0026amp; Liepert, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and increased subsidence over the region (Seager et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). During this season, the AAer signal, although weak and statistically insignificant, also exhibits a drying trend. Despite both forcings acting in the same direction, the strong internal variability in the region (Supplementary Fig.\u0026nbsp;3d) hinders the detection of these externally forced signals (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec; Campos et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Pe\u0026ntilde;a-Angulo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Vicente-Serrano et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In summer, the contrasting responses to GHG and AAer forcings\u0026mdash;GHG-induced drying versus AAer-induced wetting\u0026mdash;help explain the weaker and often statistically insignificant precipitation trends in the ALL-forcing estimates (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). The wetting response associated with AAer may reflect aerosol\u0026ndash;cloud interactions during a season dominated by convective precipitation (Christidis \u0026amp; Stott, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These opposing effects add another layer of complexity, further hindering the robust detection of an anthropogenic signal in summer precipitation. Despite the reduction in AAer emissions during 1981\u0026ndash;2020, as previously discussed, the summertime precipitation response remains positive in most sub-regions (Supplementary Fig.\u0026nbsp;5d).\u003c/p\u003e\u003cp\u003eOverall, the results reveal a robust anthropogenic imprint on temperature trends across the WMed, mainly associated with GHG forcing, whereas precipitation changes remain uncertain and spatially variable, reflecting the combined effects of strong internal variability and compensating external forcings. This study focuses on regional-scale analyses to better capture the spatial heterogeneity of climatic responses across the WMed; nevertheless, this approach also entails certain limitations. The use of small sub-regions, while necessary to represent the WMed climatic heterogeneity, may enhance the relative contribution of internal variability, thereby complicating the detection of externally forced signals. This regional focus also poses challenges for global climate models, which still operate at relatively coarse spatial resolutions. Improving model resolution and optimising their representation of regional processes remain essential steps for advancing attribution studies at this scale. Nevertheless, in this study, no clear relationship was found between model resolution and the ability to reproduce observed trends (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Other aspects of sub-regional climate change in the WMed call for further investigation, particularly the behaviour of extreme events under continued global warming and their link to changes in atmospheric dynamics and physical processes. These aspects will be explored in future work to provide a more comprehensive understanding of the mechanisms driving regional climate responses.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Data and Model Evaluation\u003c/h2\u003e\u003cp\u003eFor this study, monthly temperature and precipitation observational and simulated data have been used for the WMed region (10\u0026deg;W-25\u0026deg;E; 33\u0026deg;-45\u0026deg;N, see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). For temperature, observations were taken from the ECMWF Reanalysis version 5 (ERA5, Hersbach et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Berkeley Earth (Rohde \u0026amp; Hausfather, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and the Climate Research Unit version TS4.07 (CRU; Harris et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while for precipitation, ERA5, CRU and the Global Precipitation Climatology Centre version 2022 (GPCC; Schneider et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) databases were used.\u003c/p\u003e\u003cp\u003eMonthly temperature and precipitation data from 11 CMIP6 models were obtained (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003eHistorical\u003c/em\u003e CMIP6 experiments span from 1850 to 2014 (Eyring et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); therefore, to complete the 1951\u0026ndash;2020 period, the time series were extended using the SSP2-4.5 future scenario (Riahi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, a 500-year pre-industrial time series was obtained for each model from its \u003cem\u003epiControl\u003c/em\u003e simulations. Historical single-forced simulation data from the DAMIP experiments (Gillett et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) were used. The experiments considered in this study are the well-mixed greenhouse-gas-only (GHG), anthropogenic-aerosol-only (AAer), and natural-only (Nat) scenarios. For the \u003cem\u003eAAer\u003c/em\u003e runs, BC, OC, SO2, SO4, NOx, NH3, CO, and NMVOC are considered. Similarly, for the \u003cem\u003eNat\u003c/em\u003e experiments, solar irradiance and stratospheric aerosols are included. For details, the reader is referred to Gillett et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The number of runs for each experiment and model is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All data from the models were regridded to a common 1\u0026deg; x 1\u0026deg; grid using a linear interpolation method with the Earth System Model Evaluation Tool (ESMValTool; Righi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCMIP6 models used in this study and number of members.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNative resolution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eNumber of ensemble members\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHistorical\u0026thinsp;+\u0026thinsp;SSP2-4.5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGHG-only\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAAer-only\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNat-only\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eACCESS-CM2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25\u0026deg; x 1.875\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eZiehn et al. 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(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHadGEM3-GC31-LL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25\u0026deg; x 1.875\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAndrews et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25\u0026deg; x 2.5\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBoucher et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMIROC6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.406\u0026deg; x 1.406\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTatebe et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMRI-ESM2-0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.125\u0026deg; x 1.125\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYukimoto et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorESM2-LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.25\u0026deg; x 3.75\u0026deg;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBentsen et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe WMed was divided into nine sub-regions based on the clustering by Campos et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). In that study, monthly temperature and precipitation data were reduced through Empirical Orthogonal Functions (EOFs), retaining 90% of the total variance, and then used as input data for a k-means clustering procedure. Several sensitivity analyses were performed, focusing on the number of clusters and the climatic homogeneity of the sub-regions obtained. For more details, the reader is referred to Campos et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To assess whether both models and observational datasets can distinguish between these sub-regions, the Rank-Sum test (Mann \u0026amp; Whitney, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1947\u003c/span\u003e) was applied at a 5% significance level to the probability distribution functions of monthly temperature and precipitation, evaluated separately. Additionally, observed and simulated sub-regional means and variances were compared to further characterise the sub-regional differences.\u003c/p\u003e\u003cp\u003eSince sub-regional trends summarise the spatial patterns of temperature and precipitation trends over the WMed, they can serve as a basis for evaluating the ability of models to reproduce these patterns. Taylor diagrams were constructed to quantify the statistical similarity between the observed mean reference and the different models, reporting the Pearson correlation coefficient, the normalised standard deviation, and the root mean squared error (Taylor, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The analysis was based on n\u0026thinsp;=\u0026thinsp;36 data points, corresponding to nine regions across four seasons, with the observed mean reference computed as the average of all observations. The ensemble mean of each model was used for this analysis. Based on the Taylor diagrams, a subset of models was selected for temperature and precipitation to be used in the subsequent detection and attribution analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Detection and Attribution Methods\u003c/h2\u003e\u003cp\u003eTo assess whether temperature and precipitation change signals have emerged from internal variability in recent decades, the Signal-to-Noise Ratio (SNR) approach (e.g., Chemke \u0026amp; Coumou, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) was applied. The signal was defined as the trend in temperature and precipitation from 1951 to each year up to 2020, while the noise was estimated by the standard deviation of all trends of the same length as signals derived from the pre-industrial control runs of each model. The SNR was obtained as the mean SNR value across all pre-industrial runs. Additionally, the Time of Emergence (ToE) approach (Hawkins \u0026amp; Sutton, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) was used to indicate when the trends emerge from internal variability. The ToE was defined as the year in which the signal exceeded 2 standard deviations of internal variability (|SNR| \u0026gt;2).\u003c/p\u003e\u003cp\u003eTo attribute recent trends in the WMed to anthropogenic emissions, the Fraction of Attributable Risk (FAR) approach (Stott et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) was applied to the sub-regional trends. Trends for the period 1951\u0026ndash;2020 were calculated for all ensemble members from the temperature and precipitation model subsets, using both the \u003cem\u003eHistorical\u003c/em\u003e (all forcings) and \u003cem\u003eNat\u003c/em\u003e (natural-only forcings) experiments. For each set of trends, a Gaussian distribution was fitted. These distributions were then used to estimate the probability of exceedance for the observed trends (represented by the mean of observations) and the simulated trends (from the \u003cem\u003eHistorical\u003c/em\u003e multi-model ensemble mean) (see Supplementary Fig.\u0026nbsp;4 for an example). The probability of exceedance under all forcings is denoted as P\u003csub\u003eALL\u003c/sub\u003e (from the \u003cem\u003eHistorical\u003c/em\u003e ensemble), and under natural-only forcings as P\u003csub\u003eNAT\u003c/sub\u003e (from the \u003cem\u003eNat\u003c/em\u003e ensemble). The FAR was calculated as 1 - P\u003csub\u003eNAT\u003c/sub\u003e / P\u003csub\u003eALL\u003c/sub\u003e, representing the probability that a given trend is attributable to anthropogenic emissions. A FAR greater than 0.5 indicates that the probability of exceedance has more than doubled due to anthropogenic influence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Contributions by external factors\u003c/h2\u003e\u003cp\u003eTo investigate the influence of selected forcing factors on recent trends over the WMed (in this case, greenhouse gases, anthropogenic aerosols, and natural forcings), a statistical detection and attribution approach based on additive decomposition and maximum likelihood estimators was used. This approach was proposed by Ribes et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as an alternative to the optimal fingerprints based on linear regression (e.g., Allen \u0026amp; Stott, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Hegerl et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ribes et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and has been applied in studies at a sub-regional scale (De Abreu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The method is based on the additivity assumption, in which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}^{*}\\)\u003c/span\u003e\u003c/span\u003e represents the true observed climate response of the climate system to a number (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:nf\\)\u003c/span\u003e\u003c/span\u003e) of true individual external forcings (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}^{*}\\)\u003c/span\u003e\u003c/span\u003e) taken together as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}^{*}={\\sum\\:}_{i=1}^{nf}{X}_{i}^{*}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Y\\:=\\:{Y}^{*}+\\epsilon\\:y,\\:\\:\\:\\:\\epsilon\\:y \\sim N(0,\\varSigma\\:y)\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{X}_{i}={X}_{i}^{*}+\\epsilon\\:x,\\:\\:\\:\\:\\:\\:\\:\\:\\epsilon\\:x\\sim N(0,\\varSigma\\:{x}_{i}),\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:i=1,...,nf\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e is the observation vector with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:y\\)\u003c/span\u003e\u003c/span\u003e observational uncertainty, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the simulated individual forcing vector with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:x\\)\u003c/span\u003e\u003c/span\u003e model uncertainty, with their respective covariance matrices \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:y\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varSigma\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e. It is assumed that the observational error arises from the internal variability, while the model error arises from the internal variability and model uncertainty. Both uncertainty distributions are assumed Gaussian, so \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}^{*}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}^{*}\\)\u003c/span\u003e\u003c/span\u003ecan be estimated using Maximum Likelihood Estimators (MLE, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{Y}}^{*}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{X}_{i}}}^{*}\\)\u003c/span\u003e\u003c/span\u003e) as described in Ribes et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As in De Abreu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the consistency with internal variability, as well as with different sets of external forcings, is assessed using a \u0026#120652;2 test (Wilks, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTrends are estimated using the best estimates \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{Y}}^{*}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{X}_{i}}}^{*}\\)\u003c/span\u003e\u003c/span\u003e. Confidence intervals are then constructed by computing trends from 4000 randomly generated vectors, from multivariate normal distributions using the mean and covariance matrices of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{Y}}^{*}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\widehat{{X}_{i}}}^{*}\\)\u003c/span\u003e\u003c/span\u003e, respectively.\u003c/p\u003e\u003cp\u003eIn this study, instead of being the observation vector, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e was derived from the \u003cem\u003eHistorical\u003c/em\u003e multi-model ensemble mean of the corresponding model subset for temperature and precipitation, following the methodology described above. The simulated individual forcing vectors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}^{}\\)\u003c/span\u003e\u003c/span\u003ewere obtained from the DAMIP experiments, using the same model subset. The number of ensemble members per model is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Similarly, the internal variability matrix was obtained from the pre-industrial control runs. The vectors \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y,{X}_{i}^{}\\:Z\\)\u003c/span\u003e\u003c/span\u003e were then obtained using the decadal averages for each season and sub-region in the WMed region. A similar exercise using the observations to derive \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Y\\)\u003c/span\u003e\u003c/span\u003e was performed, yielding similar results (not shown).\u003c/p\u003e\u003cp\u003eThe analysis was performed using the Python package described in De Abreu et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is publicly available on GitHub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rafaelcabreu/attribution/\u003c/span\u003e\u003cspan address=\"https://github.com/rafaelcabreu/attribution/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003e7. Author Contribution Statement\u003c/h2\u003e\u003cp\u003eDC, MO, and FDR designed the study. DC developed the diagnostics and analyses and wrote the initial manuscript. MS downloaded and organised the data. DC, MO, PC, and FDR contributed to the interpretation and discussion of the results and the improvement of the manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003e5. Funding Statement\u003c/h2\u003e\u003cp\u003eThis work was supported by the CLIMCAT project: \u0026ldquo;Plan for comprehensive climate change information for Catalonia\". MO is funded by the AI4Science PN070500 fellowship within the \u0026ldquo;Generaci\u0026oacute;n D\u0026rdquo; initiative, Red.es, Ministerio para la Transformaci\u0026oacute;n Digital y de la Funci\u0026oacute;n P\u0026uacute;blica, for talent attraction (C005/24-ED CV1). Funded by the European Union NextGenerationEU funds, through PRTR.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDC, MO, and FDR designed the study. DC developed the diagnostics and analyses and wrote the initial manuscript. MS downloaded and organised the data. DC, MO, PC, and FDR contributed to the interpretation and discussion of the results and the improvement of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded by the CLIMCAT project: \u0026ldquo;Plan for comprehensive climate change information for Catalonia\".\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eMonthly ERA5 data is available from the Copernicus Climate Change Service (Copernicus Climate Change Service, 2019). CRU monthly temperature and precipitation data are available from the Climatic Research Unit webpage ( [https://crudata.uea.ac.uk/cru/data/hrg/#info](https:/crudata.uea.ac.uk/cru/data/hrg) ), Berkeley Earth monthly temperature is available from the Berkeley Earth data webpage ( [https://berkeleyearth.org/data/](https:/berkeleyearth.org/data) ), GPCC monthly precipitation data is available from the DWD webpage [(https://www.dwd.de/EN/ourservices/gpcc/gpcc.html](https:/www.dwd.de/EN/ourservices/gpcc/gpcc.html) ). The CMIP6-DAMIP model outputs used in this study are available online in [https://esgf-node.llnl.gov/search /cmip6/](https:/esgf-node.llnl.gov/search)All the Jupyter Notebooks and codes used for the analysis will be available in a GitHub repository after acceptance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAli, E., Cramer, W., Carnicer, J., Georgopoulou, E., Hilmi, N., Le Cozannet, G., \u0026amp; Lionello, P. (2022). 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The Australian Earth System Model: ACCESS-ESM1.5. \u003cem\u003eJournal of Southern Hemisphere Earth Systems Science\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(1), 193\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1071/ES19035\u003c/span\u003e\u003cspan address=\"10.1071/ES19035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7904665/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7904665/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Western Mediterranean (WMed) is one of the regions most affected by climate change, where the interplay between anthropogenic forcings and strong natural variability produces complex spatial and seasonal patterns of change. This study analyses the anthropogenic imprint on 1951\u0026ndash;2020 seasonal temperature and precipitation trends across climate-derived sub-regions of the WMed using multiple detection and attribution methods. A performance-based filtering of CMIP6 models was implemented to ensure an adequate representation of observed regional trends prior to attribution analyses. Detection and attribution were assessed using CMIP6 DAMIP single-forcing experiments through the Signal-to-Noise Ratio (SNR), the Fraction of Attributable Risk (FAR), and a statistical optimal fingerprinting method. Results reveal a robust anthropogenic imprint on temperature, with the amplitude of forced signals exceeding twice that of internal variability across all sub-regions. Greenhouse gas forcing emerges as the dominant driver of warming in both summer and winter, while anthropogenic aerosols exert a cooling effect that partially offsets greenhouse gas\u0026ndash;induced warming. In contrast, precipitation trends remain within the bounds of internal variability, although detectable drying signals associated with greenhouse gas forcing appear over northern Africa and the southwestern Iberian Peninsula in winter. In summer, precipitation trends show contrasting responses to greenhouse gas and aerosol forcings. These findings highlight the value of regional-scale attribution frameworks and model performance filtering for reducing uncertainty in Mediterranean climate analyses, providing a basis for further attribution studies in this highly vulnerable region.\u003c/p\u003e","manuscriptTitle":"Anthropogenic Influence on Temperature and Precipitation Trends in the Western Mediterranean: A Multi-Method Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-07 12:55:58","doi":"10.21203/rs.3.rs-7904665/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"382798ca-f2fb-4483-9608-04cddebd41e1","owner":[],"postedDate":"November 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57486468,"name":"Earth and environmental sciences/Climate sciences"},{"id":57486469,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2025-12-28T17:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-07 12:55:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7904665","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7904665","identity":"rs-7904665","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}