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# Using artificial intelligence to identify CMIP6 models from daily SLP maps

Pascal Yiou, Soulivanh Thao

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Pascal Yiou, Soulivanh Thao. Using artificial intelligence to identify CMIP6 models from daily SLP maps. npj climate and atmospheric science, 2025, 8 (1), pp.366. 10.1038/s41612-025-01246-y. hal-05370347

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# Using artificial intelligence to identify CMIP6 models from daily SLP maps

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Pascal Yiou<fnref n="1" />,²✉ & Soulivanh Thao<fnref n="1" />,²

The chaotic nature of the climate has motivated the production of large ensembles of simulations. To exploit those ensembles for impact or attribution studies, models are either considered separately or pooled together to increase the statistical significance of the results. The latter assumes that models are interchangeable. Care is necessary when fields, like temperature, yield obvious biases. Synoptic fields like SLP do not yield obvious biases, which might justify their use to enrich reanalysis data. Here, we examine this hypothesis through a neural network classification approach. The goal is to determine whether it is possible to recognize a climate model from a single SLP map over the North Atlantic. We find that models are highly identifiable in the summer (and less so in other seasons). From this classification, we identify families of climate models and investigate how climate change can affect SLP daily patterns toward the end of the 21st century. This study allows identifying which climate models could be used as input for AI-based forecasts.

Describing and understanding climate variability poses major computational, statistical, and physical challenges. Some of the issues are treated by considering ensembles of multiple global climate model (GCM) simulations, i.e., with several climate models, and running several simulations. Then averages and other statistical moments are computed from those large datasets<fnref n="1" />. Another ambition that motivates the generation and use of those large climate datasets is to consider them as surrogates of the real world to learn how to forecast the weather², or provide extreme event attribution statements³. Those endeavors (computing statistical moments, transfer learning) make the implicit assumption that all climate models yield similar probability distributions, and that those statistical properties are the same as observations⁴. Climate model biases violate this assumption. There are many ways of removing those biases, in order to get close to this important assumption⁵,⁶, but those methods are rarely devised for fields (with space and time).

Even if a meteorological field obtained from a GCM does not yield obvious biases, the question we want to address is whether it is possible to recognize a GCM from *one* daily map, like sea-level pressure (SLP). If one can provide such an identification, then learning (in the machine learning sense) from one GCM might not be directly transferable to other models. If GCMs cannot be identified, even at short time scales, then one has a good case for pooling them in order to enlarge the set of observed climate variability that is limited to a few decades. One of the motivations of this paper stems from the idea of transfer learning⁷,⁸, from ensembles of climate model simulations, to infer features of actual weather predictions. In this

perspective, learning from a daily timescale is crucial in order to investigate extreme events⁹.

In this paper, we consider a large selection of simulations from the CMIP6 archive¹⁰, which contains 47 models that provide data with daily time steps (Supplementary Fig. S1). We consider daily SLP over the North Atlantic, between 1970 and 2000. We determine whether it is possible to recognize a model name from *one* daily SLP map (over the North Atlantic). SLP is a sufficient field to reconstruct temperature fields over Europe¹¹. We use a simple artificial intelligence (AI) algorithm of classification (a multi-layer-perceptron neural network¹²) without any particular tuning to learn (in the machine learning sense) how to recognize a model from SLP features, for each season (summer, autumn, winter, and spring). The classification is done along with the ERA5¹³ and NCEP¹⁴ reanalyses. Hence, this study extends the garment identification AI challenge (i.e., identifying shoes, pants, shirts, etc. from pictures¹⁵) to a more sophisticated setting. The analysis protocol is described in the methods section.

If an AI algorithm cannot tell models apart (from daily SLP fields) or from reanalyses, then pooling model SLP can be considered a promising endeavor in order to enlarge the sample size in order to build statistical confidence. If an AI algorithm can tell models apart, even though their sample means and standard deviations are similar, then it is not possible to directly extrapolate what the AI algorithm has learned (in the machine learning sense), even from a large simulation ensemble of one model, to infer some features of the real world.

We evaluate whether GCMs can be identified from simulations that are provided in a different setting from the learning set (different ocean model,

<footnote n="1">¹Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France. ²Institut Pierre-Simon Laplace, Sorbonne Université, Paris, France. ✉e-mail: pascal.yiou@lsce.ipsl.fr</footnote>
different atmospheric parameterizations, etc.). This study revisits the analyses of refs. 16,17 by focusing on daily time scales, which adds a major difficulty to the classification problem, due to meteorological variability. It also treats a climate variable that yields less obvious biases than surface temperature¹⁸.

We determine whether a warmer climate, according to SSP scenarios¹⁹ affects the weather patterns of climate models. This is done with the SSP5-8.5 scenario for the end-of-the century (2070–2100) period. The Methods section explains the analysis protocols.

## Results

### SLP classification of 16 models

Each daily SLP map was classified onto 16 GCMs and the ERA5¹³ reanalysis (see the “Methods” section and Supplementary Table S1). The classification was done for the four seasons (Summer: June–July–August

(JJA); Fall: September–October–November (SON); Winter: December–January–February (DJF); Spring: March–April–May (MAM)), due to the seasonality of the atmospheric circulation²⁰.

The training set used ≈22 years for each model, and the validation set used the remaining ≈8 years. The training was repeated 20 times, so that 20 AI models were obtained. We report the resulting scores on the validation periods in Fig. 1. This reflects the variability of classification scores due to the algorithm itself. In the following analyses, we will keep the AI models that yield the best overall scores.

The summer (June–July–August) classification of 16 models and ERA5 reanalysis shows that the AI algorithm can recognize seven models with a success probability larger than 0.6 (Fig. 1a). It is possible to enhance the classification score by repeating the neural network training with random samples of 9 GCMs rather than 16. This guarantees that the

<figure><img src="image_5.png" /><figcaption>**Fig. 1 | Classification probability distributions.** Empirical probabilities that 16 CMIP6 models or ERA5 are correctly classified for each season (panels a–d). 20 training classifications were performed. The boxplots reflect the variability of the</figcaption></figure>

score probabilities of the training procedure (20 classifications). The horizontal dashed line represents the threshold probabilities *p* = 0.6 and *p* = 1/17 (a uniformly random classification).
<figure><img src="image_6.png" /><figcaption>**Fig. 2 | Classification test (confusion matrix).** Empirical probabilities of classifying CMIP6 models in Supplementary Table S1 or ERA5 onto those models, for the four seasons (panels a-d). CMIP6 model simulations in the vertical axes are distinct from the training simulations in the horizontal axes. Darker colors indicate higher</figcaption></figure>

probabilities. In those diagrams, the sum of probabilities along lines is 1. Model simulations listed on the vertical axis are classified according to the list of models on the horizontal axis. In this figure, the two lists of names coincide.

classification scores exceed 80% for daily SLP (not shown). This procedure is not discussed here, as this study is based on the minimal achievable result with AI and avoiding technical tuning procedures.

For the other seasons (spring, autumn and winter) the AI algorithm classification scores are lower, and rarely pass the 0.6 value (Fig. 1b-d). This means that the SLP from 17 models (including ERA5) is difficult to attribute to one model. For DJF, four GCMs can be classified with probabilities larger than 0.6, although the ERA5 reanalysis is poorly classified. For intermediate seasons, three models (for SON) or two models (for MAM) can be classified by probabilities exceeding 0.6. The SLP from the NorCPM1 model is consistently well classified across all seasons. Conversely, the EC-Earth3 model yields consistent low classification probability rates across seasons. The ERA5 reanalysis does not stand out as "recognizable" among the CMIP6 models.

A test procedure with a run that was not used in the training step for each model, and the best AI model in Fig. 1 is performed. Figure 2 shows the classification probability distributions onto the 16 CMIP6 models (Supplementary Table S1) and ERA5, with different historical runs (the test sets). The diagonals indicate whether the AI model clearly identifies models from daily SLP fields. We note that, with the best AI model (from Fig. 1), the JJA true positive rates exceed *p* = 0.6, which means that it is possible to identify a GCM from a daily SLP map, more than 60% of the time, which is better than tossing a coin (and higher than *p*₀ = 1/17 if all models/ERA5 are equiprobable). We verified that summer SLP can be recognized, for any run of the 16 chosen CMIP6 models (listed in Supplementary Table S1) with probabilities exceeding 0.6 (not shown). This means that internal variability does not alter the SLP identification process. We also verified that no obvious bias among
GCMs explained such a classifiability by comparing mean SLP and standard deviations (Supplementary Fig. S3). The spatial structures of SLP (and its variability) are very similar between GCMs, and the naked eye would have difficulties identifying a model from a single daily SLP map.

To better understand the features used by the AI model when performing the classification, we employ an interpretability method called LRP $-e^{z^1}$ (see the “Methods” section). For each GCM and ERA5, we show the relevance scores for the best predictions (Supplementary Fig. S7) and the worst prediction (Supplementary Fig. S8). In both cases, we observe that for several climate models, the AI model bases its classifications on the SLP patterns over the Sahara, around the Mediterranean region and the Alps, and sometimes over Greenland. In some cases, we also observe wave-like patterns over the North Atlantic region where patches of high relevance alternate with low-relevance patches. The patterns identified by the neural network are often small, in terms of spatial scale, compared to the large-scale circulation patterns visible in the SLP maps. This suggests that the neural network often makes its classification choice based on small details in the SLP fields. Over the North Atlantic Ocean, we observe in some cases larger-scale relevance patterns that follow the SLP fields' spatial structure. It may indicate that the statistical distribution of the position of low and high pressure systems can be different from one GCM to another.

Those classification probabilities drop during intermediate seasons (SON and MAM), although models can still be identified, albeit with scores lower than *p* = 0.3. GCMs (or ERA5) cannot be identified from winter (DJF) daily SLP maps (Fig. 2c). The ERA5 reanalysis is not more identifiable than the GCMs. Therefore, North Atlantic SLP maps of GCMs can hardly be distinguished from reanalyses, especially in the winter.

For all seasons, the EC-Earth model is often confused with the ERA5 reanalysis (Fig. 2), which may be explained by the fact that they are based on the same atmospheric model²².

For comparison purposes, ERA5 was replaced by the NCEP reanalysis. ERA5 and NCEP differ on physical aspects (the atmospheric model) and space resolution. The training was performed with the same 16 CMIP6 GCMs. The results are qualitatively similar, with high classification rates in JJA and low rates in DJF (Supplementary Fig. S4). The main quantitative change is that EC-Earth3 family simulations are no longer classified as the reanalysis data (Supplementary Figs. S5 and S6).

## Identification of GCM family ties

Starting from a classification of 16 models in Supplementary Table S1, we classify the "sister" models identified in Supplementary Fig. S1 (black bars). Those GCMs are produced by the same research groups, but can yield different horizontal resolutions (shown in Supplementary Table S1), or contain different physical configurations¹. The goal is to check whether the classification performed on the reference models works for "sister" models, especially for the summer season.

With a couple of exceptions, family ties can be identified with SLP classification (Fig. 3). Some GCMs from different research groups (e.g., UKESM1 and HadGEM3 models) share the same atmospheric model (e.g., MOHC). The UKESM1-0-LL and HadGem3-GC31-LL models have a similar horizontal resolution, but UKESM1-0-LL contains atmospheric chemistry and ocean biogeochemistry modules. The HadGEM3-GC31-MM essentially yields a higher horizontal resolution. Both models tend to be classified as HadGem3-GC31-LL, especially in JJA (Fig. 3a). Classifiability does not increase during other seasons. Similarly, models of the EC-Earth consortium (EC-Earth3-Veg and EC-Earth3-Veg-LR) simulations are classified as the EC-Earth3 model. The CNRM-ESM2-1 and reference CNRS-ESM1 GCMs differ from their atmospheric chemistry models. This difference does not affect their SLP family classification in the summer. The NorESM2-LM and NorCPM1 GCMs essentially differ in their vertical resolution, but their family can be identified.

The horizontal resolution of MPI-ESM1-2-HR is almost twice the resolution of MPI-ESM1-2-LR (all submodels are the same, though). This explains that those two GCMs cannot be classified into one another in the summer, because the high-resolution model (which has a fairly high

resemblance to ERA5) can yield SLP patterns that are not obtained with the low-resolution GCM. Similarly, the FGOALS-I3 model is nearly twice the resolution of FGOALS-g3, with a different atmospheric code. This also explains why the summer SLPs of the two models (from the same institution) do not seem similar.

Therefore, the daily SLP maps in the summer season (JJA) allow identifying GCM families (Fig. 3a) when horizontal resolutions are comparable, while this is much less clear for other seasons. This implies that the atmospheric circulation from models of the same family can be "pooled" in order to increase ensemble sizes. This feature is linked to the intrinsic resolution of the atmospheric model, because all models and ERA5 were re-interpolated on a 1° × 1° degree grid.

## Influence of climate change

We investigate the SLP classification from the 16 models in Supplementary Table S1 toward the end of the 21st century in scenario simulations¹⁰,¹⁹. The scenario we consider here is SSP5-8.5, and we extract the SLP from 2070 to 2100. We consider the AI model trained on the historical period.

The probabilities along the diagonal in JJA (Fig. 4a) slightly decrease compared to the historical period (Fig. 2), implying a slight increase of "misclassification". This suggests that some models witness the appearance of new SLP patterns (with respect to their own 1970-2000 behavior) towards the end of the 21st century, and that those patterns were sampled in other GCMs.

The highest classification rates for intermediate seasons (SON and MAM) still appear on the diagonals of Fig. 4bd (the NorCPM1 model does not propose daily SLP for SSP simulations and hence does not appear). This identification no longer holds for winter SLP (Fig. 4c). This shows that even in a warmer climate, due to radiative increase at the surface (from the SSP), GCMs can still be identified from a "historical" learning of SLP. Other SSPs show a similar behavior (not shown). This analysis cannot detect whether climate change affects the persistence or frequency of SLP patterns.

There is one exception to this stability: the NESM3 model, whose SSP5-8.5 winter classification never finds SLP patterns in historical simulations of the same model in 1970-2000 (upper right corner in Fig. 4). This suggests that this model yields a change in winter atmospheric circulation patterns.

## Discussion

The classification results of this study are based on a fairly simple use of a neural network, without any particular tuning (e.g. low number of layers of neurons, a simple algorithm, no convolutional layer, etc.). For example, only one hidden layer of 256 neurons is sufficient to correctly classify summer SLP. The training sets (2000 days for each model) are small, compared to what is potentially available in the CMIP6 archive. This choice is made to avoid overfitting. The classification scores could be improved by considering larger training sets or tuning neural network parameters, although the dependence on the season would remain unchanged.

Using other climate fields (temperature, precipitation, wind speed) would be possible, but since they highly depend on the model's horizontal resolution and other idiosyncrasies like local biases, it is expected that model classifiability could be enhanced in a fairly trivial way. Hence, this type of analysis would be interesting to test the effects of multi-variate bias correction on a CMIP6 ensemble and reanalysis data.²³ used this strategy to design bias correction models, where a bias-correction model is trained so that a classifier is not able to recognize a bias-corrected field from a reanalysis field.

A few papers have investigated how the whole probability distributions of GCM simulations depend on the model, and hence allow identifying models⁴,¹⁶. Although our study learns (in the machine learning sense) the statistical moments of GCM simulations from a rather small training set (≈20 years), the endeavor is to identify models from a single SLP map. This complements the analysis of Brunner and Sippel¹⁸ on temperature data.

It has been argued that internal climate variability is a major source of uncertainty for climate assessments²⁴,²⁵, as members of ensemble simulations could show different behavior within a given horizon due to
<figure><img src="image_7.png" /><figcaption>**Fig. 3 | Family classification.** Empirical probabilities of classifying CMIP6 models (blue lines in Suppl. Fig. S1 onto CMIP6 models in Supplementary Table S1 or ERA5, for the four seasons (panels a to d). Darker colors indicate higher probabilities.</figcaption></figure>

Model simulations listed on the vertical axis are classified according to the list of models on the horizontal axis.

interdecadal ocean variability. Such studies hence suggest that simulations of the same climate model should be treated separately, as their variability might differ for a given time window (e.g., 1970–2000). Since CMIP6 simulations are initiated in 1850, the so-called "butterfly effect" and interdecadal variability potentially lead to very different ocean (and atmospheric) states after 1950 across all runs²⁶,²⁷. Our study brings a nuance by showing that simulation ensembles of the same model do yield an identifiable consistency, while the role of internal variability is more important in the winter season, which leads to a lack of classification skill. In particular, the summer classifiability of ensemble members (same model) is not affected by the ocean state of the model, although potentially different.

We outlined the remarkable faculty of identifying a climate model from one daily SLP map, with a simple neural network. This property is essentially

valid for the summer season. The 16 reference training models we considered are not only different from each other, but they are also different from the ERA5 reanalysis in the present-day period. It is much more difficult to differentiate the SLP from models and reanalysis for other seasons, which is also an interesting feature, because one can pool models in order to increase the sampling of data in a meaningful way. This is explained by the "in-distinguishability" hypothesis of averaged fields in GCMs⁴.

This rather simple approach also allows us to identify the family ties of climate models. This means that different flavors of the same model (increasing resolution, changing land-use schemes, or even changing the ocean model) lead to similar atmospheric properties in the midlatitude regions (at least the North Atlantic), and hence show the robustness of the atmospheric part of coupled models, especially in the summer.
<figure><img src="image_8.png" /><figcaption>**Fig. 4** | **Climate change classification.** Empirical probabilities of classifying CMIP6 models in Supplementary Table S1 or ERA5 for SSP5-8.5 simulations (2070-2100) onto those models (historical period: 1970-2000), for the four seasons (panels a to d).</figcaption></figure>

Darker colors indicate higher probabilities. Model SSP5-8.5 simulations listed on the
vertical axis are classified onto historical simulations listed on the horizontal axis.

AI-based weather forecast systems have been trained on the ERA5 reanalysis⁹,²⁸. It is tempting to use a larger database for training in order to have a better sampling of interannual atmospheric variability⁹. Our study shows that it could be misleading to take any climate model, because of the different spatial probability distributions of atmospheric variables between models, and it would require complex bias correction. This study has outlined good candidate GCMs (with large ensembles, similarities with ERA5) for this exercise. Our study provides a framework to extend the study of Cariou et al.¹¹ to other models than MIROC6. Another perspective is to evaluate the effect on “identifiability” of climate models when a bias correction procedure is applied²³. This approach is akin to a “Turing test” for climate models²⁹, especially if they have to be used (and trusted) for daily timescales.

This also implies that variations around an atmospheric model (e.g. different land use schemes, ocean models, atmospheric chemistry modules, etc.) lead to similar spatial statistical properties of the atmosphere, provided that the horizontal resolution is similar. Hence, the size of large ensemble model simulations (with the same model³⁰) can be enriched with sister model simulations. Therefore, it makes sense to “pool” climate model simulations of the same family, to perform extreme event attribution analyses³¹.

Methods

CMIP6 model selection

This study focuses on North Atlantic [50W–20E; 30N–65N] daily SLP fields
from the CMIP6¹⁰ archive. The SLP fields were extracted for all runs of
global climate models (GCMs) that propose daily fields for historical and SSP simulations¹⁹. We restricted the analysis to the data that is available on the IPSL database server, which is a subset of the ESGF data https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/. For simplicity, some models (e.g., with only one member) were not used in this study (black lines in Supplementary Fig. S1).

As a reference reanalysis, we considered the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis¹³, which has a horizontal resolution of 0.25". For comparison purposes, we also used the National Centers for Environmental Prediction (NCEP) reanalysis¹⁴, which has a horizontal resolution of 2.5".

We focus on the North Atlantic region (Supplementary Fig. S2), although other regions of the world could have been used, because this is where climate models seem to yield a large consensus³². SLP patterns marginally depend on climate change (unlike many other climate fields)³³, although the patterns yield a seasonal cycle²⁰. Hence, investigating North Atlantic SLP classification is deemed a difficult endeavor.

For training, we chose 16 GCMs (out of 47) because they yield the largest number of runs (and more than 3), and reflect the diversity of atmospheric models, as climate modeling groups can provide simulations with variations on the resolution, ocean model, etc.¹.

In the training set, we considered the first 2000 days (for each season) in historical simulations, from 1970 to 2000, which corresponds to ≈22 years (hence 1970–1992). We used the first simulations in lexicographic order for the training set. For validation, we used the remaining ≈8 years of the same simulation. The test set included historical simulations (1970–2000) from the third simulations in lexicographic order (this choice was arbitrary). Therefore, the test sets (in Figs. 2–4) are disjoint from the learning set.

For all models, the SLP fields were normalized by their average. Therefore, models cannot be identified by a potential bias in the mean, which is difficult to identify. The spatial standard deviations are rather similar across models (Supplementary Fig. S3).

All SLP fields (CMIP6, ERA5, and NCEP) are interpolated onto 1" × 1" maps. Therefore, information on the horizontal resolution of each model is not directly used in the classification.

The test sets include other runs from the same model, runs from other models (black lines in Supplementary Fig. S1), and scenario runs until the end of the 21st century. In this paper, we focused on the SSP5-8.5 (although computations were done for all SSPs³⁴). This scenario might not be the most relevant for society³⁵, but it allows a higher signal-to-noise ratio to identify responses to climate change.

## AI classification configuration

The AI classification model we use is a basic method of image classification¹², which can be used to identify clothes from pictures¹⁵.

The AI model is a simple dense neural network with a single hidden layer of 256 neurons with ReLu activation functions and an output layer of 17 neurons (the maximum number of GCMs) with a softmax activation function. The dense neural network uses as inputs SLP fields flattened into vectors of 70 × 36 = 2520 values.

The neural network model (AI model) is trained over the training set constructed from models listed in Supplementary Table S1. 5 epochs are used in the training, and sparse categorical cross-entropy is optimized, with an Adam optimizer. The training is repeated 20 times, because this procedure yields random selections of input data and weight initialization. At each new training step, we compute the score rate, i.e., the probability of correctly classifying an SLP field on a validation set, which is different from the training set. The scores of this procedure are shown in Fig. 1, for validation sets that consist of other historical simulations. For each season, we keep the AI model that yields the highest score.

Those AI models are used to classify models that are not listed in Supplementary Table S1. This helps identifying potential “family ties”, when GCMs share common atmospheric components.

Then the AI models (for historical periods) are used to classify scenario simulations (SSP5-8.5) to track potential drifts in daily SLP patterns.

In Fig. 1, we consider an arbitrary score probability of $p = 0.6 > 0.5$, which is higher than a “coin toss” probability and much higher than an equiprobable classification value over the 17 models (including the reanalysis) of $p = 1/17 \approx 0.06$.

To interpret the classification made by the AI model for specific examples, we use the layer-wise relevance propagation (LRP) methods³⁶. For the input layer and each hidden layer of the neural network and a given prediction, LRP gives a relevance score so that

$$f(x) = \sum_{i} R(x_i) = \sum_{j} R(x_{j,k}) \quad \text{for any layer, k} \quad (1)$$

where $x$ is the input, $R(x_i)$ is the relevance score of the input variable $x_i$ and $R(x_{j,k})$ is the relevance score of the neuron $j$ of layer $k$ for the predicted logit score $f(x)$ of the class of interest. Positive relevance pixels of the input image increase the logit score and thus the probability to classify the input to this class. Conversely, negative relevance pixels decrease the logit score of the class of interest. We choose the e-rule to propagate the relevance score from the output layer to the input layer. This rule smoothes out the effects of low-contributing or contradictory input pixels, to only highlight the most relevant pixels²¹. We perform the LRP analysis on two sets of SLP maps in the Summer, one for the best case of each GCM and one for the worst case of each GCM. For the best cases, we show the relevance score maps where the AI model makes the right predictions with the highest predicted probability among all SLP maps (Supplementary Fig. S7). For the worst cases, we show the relevance score maps where the AI model makes the wrong prediction but with high confidence, i.e., the AI model has assigned the highest probability (among all SLP maps) to the wrong GCM (Supplementary Fig. S8).

## Data availability

ERA5 and NCEP reanalysis data were extracted from the climate explorer (https://climexp.knmi.nl/). CMIP6 data is available on ESGF nodes (e.g., https://esgf-node.ipsl.upmc.fr/projects/esgf-ipsl/).

## Code availability

The codes for preparing CMIP6 files, and classification are available from https://github.com/pascalyiou/CMIP6_CNN-Classification.git.

Received: 18 March 2025; Accepted: 28 September 2025; Published online: 17 November 2025

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## Acknowledgements

Some ideas of this study emerged from discussions with colleagues at Météo-France (Enora Cariou, Julien Cattiaux, and Aurélien Ribes). We thank the two anonymous reviewers for their thoughtful comments that helped clarify the manuscript. We thank the IPSL ESPRI group for maintaining the CMIP6 archive. Most computations were done with the IPSL GPU server. This work received the support of the grant ANR-20-CE01-0008-01 (SAMPRACE) and from Agence Nationale de la Recherche—France 2030, as part of the PEPR TRACCS program under ANR-22-EXTR-0002. This work also received support from the European Union's Horizon 2020 research and innovation program under grant agreement No. 101003469 (XAIDA).

## Author contributions

P.Y. designed the analysis and wrote the main manuscript. S.T. provided experience on AI classification and designed the AI performance tests. All authors reviewed the manuscript.

## Competing interests

The authors declare no competing interests.

## Additional information

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41612-025-01246-y.

**Correspondence** and requests for materials should be addressed to Pascal Yiou.

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