Reliable causal inference is a central challenge in Earth and climate sciences: observational records are limited, interventions are rare or impossible, and process representations in models rely on parametrizations that can introduce strong asymmetries between variables and the causal mechanisms [1,2]. Leveraging these asymmetries, rather than treating them as nuisances, can offer a principled route to causal discovery that is directly aligned with scientific modeling practice [2].

We address bivariate causal discovery from the standpoint of equation discovery using the Bayesian Machine Scientist (BMS) framework [3]. Our key contribution is to formalize the theoretical link between Symbolic Regression (SR) and Algorithmic Information Theory (AIT) via the Minimum Description Length (MDL) principle: the more plausible causal direction is the one that admits a shorter joint description in terms of a mechanism plus independent inputs [4]. Building on this connection, we characterize the mathematical properties of the resulting causal criterion, including identifiability and asymptotic consistency, and we analyze the role of core assumptions—most notably the Principle of Independent Causal Mechanisms (ICM)—in the context of geophysical data and climate-model parametrizations [5].

We demonstrate the approach on simulated benchmarks and on real Earth-system examples covering both i.i.d. settings and time-series climate data. The results illustrate when and why asymmetric parametrizations help disambiguate causal direction, and they provide a practical pathway to turn discovered governing equations into testable causal hypotheses for Earth and climate science.

References

[1] Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. Elements of causal inference: foundations and learning algorithms. The MIT Press, 2017.

[2] Gustau Camps-Valls, Andreas Gerhardus, Urmi Ninad, Gherardo Varando, Georg Martius, Emili Balaguer-Ballester, Ricardo Vinuesa, Emiliano Diaz, Laure Zanna, and Jakob Runge. Discovering causal relations and equations from data. Physics Reports, 1044:1–68, 2023.

[3] Roger Guimera, Ignasi Reichardt, Antoni Aguilar-Mogas, Francesco A. Massucci, Manuel Miranda, Jordi Pallares y Marta Sales-Pardo. A Bayesian machine scientist to aid in the solution of challenging scientific problems. Science Advances, 6(5):eaav6971, 2020.

[4] Dominik Janzing, Joris Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniūsis, Bastian Steudel und Bernhard Schölkopf. Information-geometric approach to inferring causal directions. Artificial Intelligence, 182:1–31, 2012.

[5] Sascha Xu, Sarah Mameche, and Jilles Vreeken. Information-theoretic causal discovery in topological order. In The 28th International Conference on Artificial Intelligence and Statistics, 2025.

How to cite: Camps-Valls, G., Guimerà, R., Varando, G., Diaz, E., Cohrs, K.-H., and Sales-Pardo, M.: Causal discovery from equation discovery, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13632, https://doi.org/10.5194/egusphere-egu26-13632, 2026.

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models often suffer from long-term error accumulation, limiting their applicability to seasonal predictions and climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP delivers probabilistic weather forecasts competitive with state-of-the-art forecasting systems, while using its own assimilated initial states from a small fraction of observations. Also, it provides seasonal predictions with skill comparable to leading operational system. Finally, GAP produces stable millennial-scale climate simulations that capture variability from daily weather fluctuations to decadal oscillations.

How to cite: Yang, S., Nai, C., Boers, N., Yuan, H., and Pan, B.: GAP: a unified deep generative framework for emulating weather and climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4964, https://doi.org/10.5194/egusphere-egu26-4964, 2026.

A pressing problem exacerbated by climate change is the inability to prepare for extreme climate and weather events due to the limited historical record of observed extremes. While crucial for risk assessment and informed policy-making, a better representation of the distribution of "feasible" outcomes remains largely uncertain, with  predictions ranging at variously defined confidence levels that remain sensitive to the choice of metrics and physical assumptions. This question naturally lends itself to investigating how we can engender plausible realizations of extreme events, and thereby allow for mitigation efforts, before communities are forced to confront destructive realities. We present a time-conditioned generative framework based on a computer-vision-aided diffusion model trained on 1km $\times$ 1km precipitation fields and their trajectories over time. The output of this model is n future potential realizations of possible storm events that may unfold over the San Jacinto river basin in the south coast of Texas.

Beyond unconditional sampling, we introduce control variables that make generation decision-relevant: the model is trained to be conditional on a (duration, intensity) pair, enabling users to request ensembles spanning targeted severity regimes (e.g., short–extreme vs. long–moderate) while preserving realistic spatiotemporal structure. This yields a family of distributions over storm trajectories indexed by interpretable controls, allowing systematic stress testing of infrastructure and emergency-response plans under plausible but high-impact scenarios. 

We separate the evaluation of our approach into two complementary perspectives: (i) distribution matching for in-sample generations, and (ii) physics-based alignment with storm-based properties for out-of-sample generations. Spatiotemporal structure of storms is also benchmarked against strong baselines like the analog ensemble method, quantifying the performance of our model to realistically capture intense rainfalls. To extract evolving storm geometries, we employ a kNN-based (k-nearest neighbors) computer-vision algorithm that dynamically identifies storm shapes across time steps. Due to the probabilistic nature of diffusion models, more comprehensive envelopes of the storm intensity and trajectory can be obtained for uncertainty quantification purposes. 

Finally, we introduce a metric that jointly measures physical plausibility through features like intensity–duration structure and scaling, as well as novelty relative to the raw training data. This metric works by penalizing overfitting patterns while rewarding those that respect feasible dynamics, allowing us to define a principled way to compare generative models for extremes. Therefore, we can determine not only how realistic our generated storms are, but also how much physical diversity they contribute beyond the observed data. We present an open evaluation suite for controllable storm generation, including storm-tracking, intensity–duration diagnostics, and physical-novelty scoring.

How to cite: Tsao, V., Zaniolo, M., and Veveakis, M.: Synthetic Physics-Aware Storm Generation via Diffusion Models for Risk Analysis of Catastrophic Events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5928, https://doi.org/10.5194/egusphere-egu26-5928, 2026.

Various pollutants pose significant threats to river ecosystems. This issue is particularly critical in Taiwan, where the unique geography of short, rapid rivers makes water retention difficult, necessitating rigorous water quality monitoring. Given the complex, non-linear correlations between water quality and meteorological parameters, this study investigates the impact of different feature selection techniques and predictive models on water quality forecasting for eight rivers in Taoyuan. We utilized 14 meteorological and water quality inputs to predict six key indicators, including COD, DO, EC, NH3-N, ORP, and SS. The methodology compared four feature selection strategies—Pearson Correlation, Entropy Weight Method (EWM), Combined Weights, and Mutual Information—alongside four forecasting models: Seq2Seq LSTM, ANFIS, MLP, and Transformer.The feature selection results reveal that the Entropy Weight Method yielded the highest precision (R^2 =0.9336), surpassing the Pearson method (R^2 =0.9161). This indicates that prioritizing features based on information entropy effectively minimizes information loss during screening. Regarding predictive modeling, the Transformer model demonstrated superior stability and accuracy. While other models fluctuated, the Transformer consistently achieved the best performance with an MSE of approximately 14.86 (RMSE=3.855) and an accuracy of 82.52%, significantly outperforming the MLP and ANFIS models. This study concludes that integrating entropy-based feature selection with the Transformer model establishes a superior and highly accurate framework for water quality forecasting in Taoyuan's rivers.

How to cite: Lu, Y.-Y. and Lin, Y.-C.: Integrated Analysis of Feature Selection and Deep Learning Models for Water Quality Forecasting Based on Meteorological Parameters, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6357, https://doi.org/10.5194/egusphere-egu26-6357, 2026.

Air pollution has emerged as one of the most critical environmental health hazards globally. According to statistics from the World Health Organization (WHO) and the Global Burden of Disease Study, approximately 7 million premature deaths occur annually due to air pollution. Fine particulate matter (PM2.5), capable of penetrating deep into the lungs and entering the bloodstream, has been confirmed to be highly correlated with ischemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), and lung cancer. Given its serious threat to public health, establishing high-precision PM2.5 prediction models is critical for early warning systems and health protection.

Addressing the common issue of missing values in environmental monitoring data, this study proposes a data preprocessing framework that combines Principal Component Analysis (PCA) for feature dimensionality reduction with the GRU-D model for time-series imputation. Testing confirms that this method effectively reconstructs data features without causing excessive smoothing. In terms of predictive modeling, this study incorporates East Asian-scale atmospheric pressure field data as a key environmental variable to capture the impact of large-scale weather systems on local air pollution. The performance of three advanced deep learning models—LSTM+CNN, PatchTST, and iTransformer—is evaluated and compared.

The results indicate that, when considering multivariate factors and long- and short-term dependencies, the iTransformer model demonstrates superior predictive performance with an R² of 0.91, exhibiting exceptional non-linear feature extraction capabilities. In comparison, both the LSTM+CNN and PatchTST models achieved an R² of approximately 0.86. Based on the iTransformer's advantages in handling large-scale meteorological features and high-dimensional time-series data, this study employs it as the core model to further extend PM2.5 concentration predictions across Taiwan, aiming to provide a valuable scientific reference for regional air quality management.

How to cite: Chuang, Y. and Lin, Y.-C.: Spatiotemporal Prediction of PM2.5 in Taiwan Using iTransformer and Large-Scale Atmospheric Pressure Features, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6363, https://doi.org/10.5194/egusphere-egu26-6363, 2026.

In recent years, climate change has led to a clear increase in both the frequency and intensity of extreme weather events. Taiwan lies along major typhoon tracks in the western North Pacific, where typhoons represent one of the most significant natural hazards. The strong winds and heavy rainfall associated with typhoons frequently cause flooding, agricultural losses, and damage to critical infrastructure. In practice, however, the severity of typhoon-related disasters does not always correspond to traditional typhoon intensity classifications based primarily on central pressure and wind speed, indicating that wind-based classifications alone may not adequately represent actual disaster impacts.

This study utilizes hourly meteorological station observations to investigate the wind and rainfall characteristics of historical typhoon events in Taiwan. Multiple machine learning and regression models are applied, together with residual analysis, to quantify typhoon characteristics and construct a Typhoon Type Index (TTI). Based on the relative behavior of wind and rainfall during individual events, different typhoon types are further examined to identify their occurrence patterns and characteristic differences across historical cases.

The results indicate that the TTI derived from machine learning–based classification models can effectively improve upon previous TTI formulations established using regression models alone. Moreover, typhoons with different wind–rainfall characteristics are associated with distinct patterns of disaster impacts, and in some cases, rainfall intensity better reflects disaster severity than wind speed. By offering an alternative perspective to conventional intensity-based classifications, this study contributes to improved typhoon disaster risk assessment and provides useful insights for future disaster mitigation and preparedness strategies.

How to cite: Huang, S.-H. and Lin, Y.-C.: Improving the Typhoon Type Index by Integrating Strong Wind and Heavy Rainfall Using Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6364, https://doi.org/10.5194/egusphere-egu26-6364, 2026.

How to cite: Dewey, M., Wilcox, L., Samset, B., and Ekman, A.: Deep-AeroGP: deep kernel learning for projecting the regional climate response to anthropogenic aerosol emission changes , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8307, https://doi.org/10.5194/egusphere-egu26-8307, 2026.

The emergence of global km-scale climate models challenges traditional model evaluation approaches, which typically rely on long climatological averages. The substantial computational costs and enormous data volumes associated with km-scale simulations often constrain simulation length, limiting the availability of long-term averages. As a result, conventional analysis methods become less practical and less informative when assessing short, high-frequency model output that is potentially dominated by internal variability. At the same time, recent advances in machine learning (ML), particularly in deep neural networks, offer new and innovative ways to efficiently extract information from large climate datasets. Building on this progress, we present an ML-based framework for evaluating climate models on a regional scale over short periods, focusing on daily near-surface air temperature fields over Europe.

We train a convolutional neural network (CNN) to distinguish spatial temperature fields from a large set of climate models. We employ 28 regional simulations from EURO-CORDEX and two global km-scale models from nextGEMS and Destination Earth. Beyond the classification based on climate model simulations, the pre-trained CNN is applied to observation-based test datasets. This setup allows us to build towards an evaluation metric, as the model, the observation-based datasets are more frequently assigned to, might be considered most similar to observed climate. Despite the regional focus of EURO-CORDEX, observation-based samples are most frequently classified as the global km-scale model IFS-FESOM. This suggests that this global km-scale model may capture regional temperature patterns more accurately than regional climate model simulations. Although our results are consistent with traditional metrics in identifying IFS-FESOM as the best-performing model, they also indicate that CNN-based evaluation provides additional information about the similarity between models and observations. 

To better understand which spatial features influence the CNN’s classification for observation-based samples, we apply explainable artificial intelligence (XAI) methods, specifically layerwise relevance propagation (LRP), to the classification outcomes. The resulting relevance patterns indicate that static features such as orography and coastlines, as well as relevance hotspots potentially linked to regions of dynamic variability, play a dominant role in the classification. This highlights that the CNN is sensitive to physically meaningful structures that define model-specific spatial fingerprints.

Using our ML-based framework, we show that a CNN can robustly distinguish between climate models on regional and short time scales as well as identify the model closest to observations. More broadly, we demonstrate that ML, combined with XAI, offers a scalable and physically interpretable approach for evaluating high-resolution climate models, thereby complementing established evaluation frameworks.

How to cite: Meindl, M., Kornblueh, M., Brunner, L., and Voigt, A.: Using Explainable AI to uncover physically meaningful features in km-scale climate models on a regional scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9427, https://doi.org/10.5194/egusphere-egu26-9427, 2026.

Africa and South America together account for more than 70 % of the global burned area representing nearly 65 % of global fire-related carbon emissions (van der Werf et al., 2017). Beyond carbon release, wildfires emit large amounts of dust and aerosols that influence regional climate through radiative processes. More generally, wildfires strongly modify land surface properties, including vegetation composition, soil carbon stocks, or surface albedo, with far-reaching consequences for regional carbon, water, and energy cycles.

In the ISBA land surface model (Delire et al., 2020), burned area is currently parameterized using grid-cell surface characteristics, a fire-resistance coefficient, soil moisture, and available biomass. While computationally efficient, this simplified formulation may contribute to persistent regional biases in simulated fire activity. To overcome these limitations, we develop a data-driven fire modeling framework based on two artificial neural network architectures: one addressing a regression task and the other a classification task. The models use meteorological conditions, vegetation states, and anthropogenic factors to estimate the daily burned area fraction.

The proposed framework reproduces the spatiotemporal variability of burned areas with some fidelity. It is specially the case in important areas such as Africa, South America, and Australia. These results highlight the potential of deep learning approaches to enhance wildfire representation and prediction in Earth system models. That would be the very future of our research project.

How to cite: Rougier, H., Decharme, B., and Mallet, M.: Modeling of burned areas on a global scale using statistical learning methods, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11185, https://doi.org/10.5194/egusphere-egu26-11185, 2026.

Application of deep learning has proved useful in many scientific domains and has also gained increased interest as a tool for weather and climate modeling in recent years. Deep Learning weather models have already demonstrated competitive prediction performance to state-of-the-art methods while hybrid models and emulators have shown some promise for climate simulation. However, the realism of simulated climate variability, and climate modes of pure deep learning models trained only on observational or reanalysis data, has not received as much attention.
As one example of these models, we investigate DLESyM, an autoregressive deep learning model based on the U-Net architecture and originally trained on ERA5 reanalysis data from 1981 to 2017 (REF1). Unlike many weather-generating deep learning models, DLESyM does not draw on sea-surface temperatures as boundary conditions, but learns to generate ocean surface patterns. Its applications could, therefore, extend to free-running simulations. The original authors showed its ability to generate stable climate simulations for time-spans up to three millenia, with the absence of spurious drifts and unphysical smoothing in the annual cycle. Here we test how realistic the simulated climate variability of DLESyM is, focusing on interannual to centennial spatio-temporal modes of internal climate variability. We seek to identify whether it is able to generalize to the underlying physical processes of the climate system, or if it is only capable of reproducing spatio-temporal statistical patterns of its training data. We compare the unforced variability of the deep learning model to that in equilibrium simulations out of General Circulation Models out of the Coupled Model Intercomparison Project phase 6 (CMIP6 GCMs), and palaeoclimate reconstructions (REF2). We focus on regional and global power spectra of surface temperatures, and gradients between land and ocean, tropics and extratropics, as well as the high latitudes. To assess the model’s ability to generalize outside the distribution of the training data we perform simulations from varying initial conditions, and comparing them with the output of CMIP6 GCMs. Based on this we discuss potentials and limitations of such a purely data-driven model for climate simulations and future climate risk assessment, where characteristics beyond mean state and slow changes become relevant.

REF1 Cresswell-Clay, N., Liu, B., Durran, D. R., Liu, Z., Espinosa, Z. I., Moreno, R. A., & Karlbauer, M. (2025). A deep learning Earth system model for efficient simulation of the observed climate. AGU Advances, 6, e2025AV001706. https://doi.org/10.1029/2025AV001706

REF2 Laepple, T., Ziegler, E., Weitzel, N. et al. (2023) Regional but not global temperature variability underestimated by climate models at supradecadal timescales. Nat. Geosci., 16, 958–966. https://doi.org/10.1038/s41561-023-01299-9

How to cite: Jansen, H., Racky, M., and Rehfeld, K.: Testing the realism of interannual to centennial climate variability in a generative coupled atmosphere-ocean deep learning model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11509, https://doi.org/10.5194/egusphere-egu26-11509, 2026.

How to cite: Rodriguez Pinilla, M., Benitez Benavides, M., Exarchou, E., Margalef, T., and Panadero, J.: Deep Learning Downscaling of Precipitation and Temperature Climate Data for Future Wildfire Risk Assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12404, https://doi.org/10.5194/egusphere-egu26-12404, 2026.

Climate models typically operate at coarse spatial resolution (~100 km) due to computational constraints, yet many climate-change impact assessments require fine-scale information (<10 km). In this study, we systematically benchmark three state-of-the-art machine-learning approaches for statistical downscaling, using the storm-resolving ICON NextGEMS dataset as reference. All methods take coarse-resolution climate fields as input and generate physically plausible high-resolution predictions. We compare: (1) UNet, a deterministic encoder–decoder architecture; (2) CorrDiff, which augments the UNet backbone with a diffusion model to produce probabilistic ensembles; and (3) CorrDiff++, which replaces diffusion with flow-matching to improve sampling efficiency. We perform 10× downscaling (0.56° to 0.056°) over central Europe for six surface variables, including temperature, wind, and precipitation. The models are evaluated along multiple dimensions: deterministic accuracy (bias, correlation), probabilistic skill (ensemble reliability and sharpness), and physical realism (energy spectra, temporal coherence, representation of extremes). Our results highlight fundamental trade-offs between computational cost, physical consistency, and uncertainty quantification. These insights provide guidance on when the additional complexity of generative models is justified for climate science applications.

How to cite: Debeire, K., Eyring, V., and Thuerey, N.: Benchmarking Deterministic and Generative Machine Learning Models for Statistical Climate Downscaling over Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12407, https://doi.org/10.5194/egusphere-egu26-12407, 2026.

The timing of the first ice-free Arctic summer is a key indicator of climate change, yet projections remain highly uncertain due to inter-model spread, internal variability, and systematic model biases. We develop a prototype framework that combines machine-learning-based methods with causal diagnostics to assess how different bias-correction and emulation approaches influence projections of the first year of ice-free Arctic conditions. Linear scaling is used as a statistical baseline to provide a transparent reference for evaluating more complex machine-learning-based approaches.

Building on recent analyses of the drivers of summer Arctic sea-ice extent at the interannual time scale, we analyse CMIP6 multi-model large ensembles to quantify relationships between September Arctic sea-ice extent and its dominant drivers, including preceding winter sea-ice volume, Arctic near-surface air temperature, and ocean heat transport. Machine-learning-based regression and emulation models are applied to refine model output, while causal diagnostics based on information flow are used to evaluate the physical consistency of inferred driver–response relationships.

We focus on two CMIP6 large ensembles with contrasting historical Arctic temperature biases over 1980–2014. Ensemble uncertainty is explored by partitioning ensemble members into bias-based subsets to assess the sensitivity of projected ice-free timing and inferred driver relationships. Results show that linear scaling shifts projected timing without altering causal structure, whereas machine-learning-based methods can modify ice-free year distributions and induce state-dependent changes in inferred causal relationships. These findings highlight the value of causal diagnostics for interpreting machine-learning-based climate projections and underscore the need for physically interpretable frameworks when applying data-driven methods to critical Arctic climate transitions.

How to cite: Tian, T., Richards, B., and Docquier, D.: Toward more reliable projections of an ice-free Arctic: Integrating machine learning and causal diagnostics in CMIP6 ensembles, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12525, https://doi.org/10.5194/egusphere-egu26-12525, 2026.

Probabilistic risk assessment requires large ensembles of high-resolution climate scenarios, yet generating such data is often computationally intractable. This study introduces a scalable generative framework designed to overcome the scarcity of high-fidelity climate data. We introduce the Field-Space Autoencoder, a geometric compression model that preserves the causal structure of atmospheric fields without forcing them onto regular lat-lon grids. Unlike standard deep learning approaches fixed to a single resolution, our method utilizes a multi-scale decomposition that stores a resolution-invariant latent representation. This flexibility unlocks a novel hybrid training strategy for generative diffusion: we combine the statistical robustness of multi-century, low-resolution simulations with the structural precision of limited high-resolution datasets. The resulting Compressed Field Diffusion model is capable of synthesizing atmospheric states that inherit the internal variability of the large ensemble and the spectral sharpness of the high-res ground truth. By bridging these data sources, we present a pathway to democratizing access to exascale-quality climate data through efficient, physically consistent emulation.

How to cite: Meuer, J., Witte, M., Plésiat, É., and Kadow, C.: Generative Emulation on the Sphere: Bridging the Resolution Gap with Field-Space Diffusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12592, https://doi.org/10.5194/egusphere-egu26-12592, 2026.

As climate change amplifies precipitation extremes and their societal and economic impacts, downscaling precipitation provides valuable local-scale information for risk assessment and adaptation planning.
However, deep-learning based statistical downscaling methods typically rely on high-resolution training data (e.g., radar observations), which are scarce and unevenly distributed globally, making geographic generalization a central challenge. Prior work shows large performance drops of deep-learning based downscaling models under geographic distribution shifts -- effects that remain even when considerably increasing the training data [1].
We view the geographic distribution shift as a form of subpopulation shift, where training and target samples are drawn from the same set of geographic domains but differ in their sampling frequencies. Consequently, the shift is driven primarily by changes in the prevalence of climatic regimes, rather than by changes in the conditional relationship between predictors and targets.
To improve robustness under cross-region transfer, we inject additional geographic context through Earth embeddings from geospatial foundation models (e.g., SatCLIP [2]). Potential strategies for integrating these embeddings into diffusion-based downscaling models include attention-based conditioning, feature modulation, and auxiliary conditioning networks.

[1] Harder, P., Schmidt, L., Pelletier, F., Ludwig, N., Chantry, M., Lessig, C., Hernandez-Garcia, A. and
Rolnick, D. [2025], ‘Rainshift: A benchmark for precipitation downscaling across geographies’, arXiv
preprint arXiv:2507.04930 .

[2] Klemmer, K., Rolf, E., Robinson, C., Mackey, L. and Rußwurm, M. [2025], Satclip: Global, general-
purpose location embeddings with satellite imagery, in ‘Proceedings of the AAAI Conference on Artificial
Intelligence’, Vol. 39, pp. 4347–4355.

How to cite: Schmidt, L., Lemaire, P.-L., Ludwig, N., Hernandez-Garcia, A., and Rolnick, D.: Leveraging Earth Embeddings for Generalizable Precipitation Downscaling Across Geographies, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14198, https://doi.org/10.5194/egusphere-egu26-14198, 2026.

Accurate forecasting of precipitation remains a central challenge in climate science, primarily due to the strong temporal and spatial variability of rainfall, a difficulty that is further intensified by the ongoing impacts of climate change. Recent developments in machine learning have facilitated the design of more accurate and robust predictive frameworks. In this context, the present study implements and evaluates three deep learning architectures; Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Extended Long Short-Term Memory (xLSTM); to forecast monthly precipitation at 27 meteorological stations distributed across Morocco, for lead times ranging from 1 to 4 months. The models are trained using a heterogeneous set of large-scale climatic predictors, including sea surface temperature (SST) over the Atlantic Ocean and the Mediterranean Sea, the East Atlantic pattern (EA), the Madden–Julian Oscillation (MJO), the El Niño–Southern Oscillation (ENSO), the Mediterranean Oscillation (MO), the North Atlantic Oscillation (NAO), and the Western Mediterranean Oscillation (WeMO). To identify the most influential predictors at each station, a principal component analysis (PCA)-based feature selection procedure is implemented. The results indicate that precipitation variability across the study area is predominantly controlled by the MO, NAO, and WeMO indices. Probabilistic forecasts are then generated using Monte Carlo dropout, enabling the networks to approximate Bayesian inference and thereby quantify predictive uncertainty and associated confidence intervals. Relative to conventional LSTM and GRU configurations, the xLSTM architecture exhibits superior predictive performance across all stations and lead times, with notably reduced uncertainty, particularly in the representation of extreme precipitation events. Overall, the models demonstrate robust skill in northern Morocco, with coefficients of determination (R²) ranging from 0.82 to 0.96 for a 1‑month lead time. However, predictive skill degrades toward the southern region, characterized by arid to semi-arid climatic conditions, where R² values decrease to 0.36–0.86. These results indicate that xLSTM effectively captures long-range temporal dependencies and low-frequency, high-intensity rainfall events, thereby representing a promising framework for improving probabilistic monthly precipitation forecasts in climatically heterogeneous regions such as Morocco.

How to cite: Zellou, B., Agdoud, F., and Ouatiki, H.: Probabilistic Monthly Precipitation Forecasting over Morocco Using xLSTM and Large-Scale Climate Predictors, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15554, https://doi.org/10.5194/egusphere-egu26-15554, 2026.

Machine learning–based weather prediction models have recently surpassed traditional numerical weather prediction systems on many skill metrics at regional and global scales, yet there is limited progress towards models operating on hectometric-scale resolutions. This setting is challenging both due to the cost of generating high-quality training data and the complex dynamics of important small-scale processes.

We introduce a graph neural network with Large-Eddy Simulation (LES) capabilities, to operate at hectometer horizontal resolution and sub-hourly time steps. Using 42 days of high-resolution realistic model output for the trade-wind regime over the western Atlantic, we train and evaluate the network on its ability to reproduce key mesoscale processes, with particular emphasis on cold-pool dynamics and convective triggering.

Cold pools are a crucial driver of low-level thermodynamic variability and cloudiness, and thus provide a stringent physical consistency test for models targeting hectometer scales, as they require accurate coupling between the cloud layer and the surface. Through a targeted ablation study, we quantify the relative importance of different input variables for reproducing surface temperature perturbations associated with cold pools, offering guidance for future parameterization and data selection strategies.

Finally, we show that the model can deterministically predict the evolution of cold pools over multiple successive generations, indicating that graph-based LES emulators can robustly capture the nonlinear feedbacks governing mesoscale organization in shallow convective regimes.

How to cite: Schulz, H., Oskarsson, J., and Denby, L.: ML-LES: Modeling cold-pool dynamics with graph-based neural network at hecto-meter grid-spacings, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16552, https://doi.org/10.5194/egusphere-egu26-16552, 2026.

The majority of supervised machine learning pipelines, particularly in the popular domains of natural language processing and computer vision, rely on manually annotated data. In geoscience applications, however, reference data are not necessarily derived from human annotation but could come as the output of explicit physical models or algorithms. These algorithms typically rely on simplifying hypotheses about the underlying physical processes and may be computationally expensive or applicable only to a limited subset of observations. In such circumstances, machine learning can be used to emulate explicit algorithms, with the objective of reproducing their outputs while potentially exploiting wider information pathways present in the data.

Beyond computational considerations, this hypothesis-light, data-driven framework allows for counterfactual testing by selectively removing input information and evaluating the model’s ability to recover similar predictions. For instance, in computer vision, color information can be removed by averaging RGB channels, while semantic or contextual information can be limited by progressively reducing the input patch size or by exploiting the inductive biases of different neural network architectures. In this way, one can identify additional cues in the input data linked to the physical property of interest, but also assess whether the model reproduces biases inherent to the reference algorithm. 

We explore this approach for high-resolution cloud-top height (CTH) estimation within the Clouds Decoded project, which uses Sentinel-2 (S2) multispectral observations (originally intended for land monitoring) to retrieve cloud properties. CTH can be estimated from S2 imagery using a stereo-based method that leverages the instrument’s geometry and inter-band delays. While effective, this approach is computationally demanding and relies on assumptions that restrict its applicability across diverse cloud scenes. We assess whether a neural network can learn to approximate this stereo-based CTH retrieval and analyse which textural, spectral, high-level semantic, or even geolocation-related cues the model might use to infer cloud height.

How to cite: Borne--Pons, P., Francis, A., Czerkawski, M., Campbell, J., and Bertozzi, B.: Machine learning emulation of stereo-based cloud-top height retrieval from Sentinel-2 , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18374, https://doi.org/10.5194/egusphere-egu26-18374, 2026.

Machine learning based dynamic numerical climate multi-model ensemble
weighting for high impact weather affecting the energy infrastructure
The infrastructure for renewable energy production and electrical grid itself is affected
by weather and climate conditions and vulnerable to high impact weather and
cascading events. A reliable representation of the meteorological conditions leading to
such events including their uncertainty is therefore needed for both weather and climate
time scales. Individual numerical weather and climate models exhibit systematic
strengths and weaknesses across scales, and geographic regions, despite the
differences in model physics and parametrizations. One way to tackle this and avoid
running single-model ensemble climate simulations are multi-model or poor-man
ensembles consisting of a set of different climate or weather models combined.
Ensemble mixing offers a way to mitigate these weaknesses while providing uncertainty
quantification. Simply ensemble averaging can dilute forecast and climate signals and
penalize outliers and rare extremes. Different approaches have been proposed to tackle
this problem by assigning non-uniform weights to individual model fields and
parameters, however, these methods often rely on domain knowledge such as model
dependencies [1,2].

Here, we propose a machine learning-based multi-model ensemble model-mixing
framework that is domain-agnostic and assigns spatially and temporally dynamic
weights, in addition to an error metric. The domain of interest is the Alps, which exhibit
challenging terrain and localized extreme events, e.g. precipitation extremes that are
difficult to capture in conventional climate models. The CERRA reanalysis data at ~5.5
km resolution serves as the target grid. We build a multi-model ensemble by combining
dynamically downscaled simulations of 2 m air temperature, precipitation, and wind
speed from COSMO-CLM (6 km) and WRF (10 km). Each regional model is driven by two
CMIP6 global climate models (MPI-ESM and EC-Earth) under two scenarios (SSP1-2.6
and SSP5-8.5), with an additional historical period used for training. Static information
such as orography and seasonal dependencies are considered. We evaluate the
ensemble’s performance on selected extreme events (e.g., heavy precipitation,
windstorms, heatwaves) that can (and did) harm energy infrastructure, such as the
European derecho 2022.

[1] Christensen, Jh, E Kjellström, F Giorgi, G Lenderink, and M Rummukainen. 2010.
‘Weight Assignment in Regional Climate Models’. Climate Research 44 (2–3): 179–94.
https://doi.org/10.3354/cr00916.

[2] Merrifield, Anna Louise, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto
Knutti. 2020. ‘An Investigation of Weighting Schemes Suitable for Incorporating Large
Ensembles into Multi-Model Ensembles’. Earth System Dynamics 11 (3): 807–34.
https://doi.org/10.5194/esd-11-807-2020.

How to cite: Thiele, P., Baier, K., Hasel, K., Schellander-Gorgas, T., Lehner, S., Spiekermann, R., Lampert, J., Lexner, A., and Schicker, I.: Machine learning based dynamic numerical climate multi-model ensemble weighting for high impact weather affecting the energy infrastructure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18613, https://doi.org/10.5194/egusphere-egu26-18613, 2026.

 Ocean physical and biogeochemical extremes, such as marine heatwaves (MHWs), deoxygenation, and acidification events have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent decades, the frequency, intensity and spatial extent of these extremes have been amplified (Capotondi et al., 2024; Shu et al., 2025; Gruber et al., 2021). Hence, a deeper understanding of the processes and precursors leading to extreme events remains crucial for improving and forecasting risk assessment.

In this study, we apply interpretable machine learning approaches to investigate which oceanic and atmospheric variables, as well as their lag effects, are most relevant for the extreme events in the North Atlantic, a relevant region for their occurrence in recent decades (England et al., 2025). Our framework combines high-resolution ocean model simulations with explainable artificial intelligence (XAI) techniques (He et al., 2024, Camps-Valls, 2025), allowing us to examine where, when, and which model variables are more important when identifying extreme events.

Rather than focusing on predictive skill, the emphasis of this study lies on identifying the underlying physics of precursor patterns leading to ocean extremes across different spatial and temporal scales. By integrating XAI into the analysis, this approach provides a more transparent and interpretable perspective on the decision-making processes of machine learning models, offering insights into the key variables and structures associated with the occurrence of ocean extremes. The outcomes of this study improve the interpretable assessment of potential precursors of MHWs, ocean deoxygenation and acidification extremes.

Camps-Valls, G., Fernández-Torres, M. Á., Cohrs, K. H., et al. (2025). Artificial intelligence for modeling and understanding extreme weather and climate events. Nature Communications, 16, 1919. https://doi.org/10.1038/s41467-025-56573-8

Capotondi, A., Rodrigues, R. R., Sen Gupta, A., et al. (2024). A global overview of marine heatwaves in a changing climate. Communications Earth & Environment, 5, 701. https://doi.org/10.1038/s43247-024-01806-9

England, M. H., Li, Z., Huguenin, M. F., et al. (2025). Drivers of the extreme North Atlantic marine heatwave during 2023. Nature, 642, 636–643. https://doi.org/10.1038/s41586-025-08903-5

Gruber, N., Boyd, P. W., Frölicher, T. L., et al. (2021). Biogeochemical extremes and compound events in the ocean. Nature, 600, 395–407. https://doi.org/10.1038/s41586-021-03981-7

He, Q., Zhu, Z., Zhao, D., Song, W., & Huang, D. (2024). An interpretable deep learning approach for detecting marine heatwave patterns. Applied Sciences, 14(2), 601. https://doi.org/10.3390/app14020601

Shu, R., Wu, H., Gao, Y., et al. (2025). Advanced forecasts of global extreme marine heatwaves through a physics-guided data-driven approach. Environmental Research Letters, 20(4). https://doi.org/10.1088/1748-9326/adbddd

How to cite: Radin, C., Mathis, M., Li, H., and Ilyina, T.: Explainable AI for Identifying Precursors of Extreme Oceanic Events in the North Atlantic, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19617, https://doi.org/10.5194/egusphere-egu26-19617, 2026.

High-resolution gridded climate datasets are essential for Earth system modelling and impact assessments, yet generating them from sparse, irregularly distributed station networks remains a significant challenge, particularly in regions with complex topography. This study evaluates the Spatial Multi-Attention Conditional Neural Process (SMACNP), a probabilistic deep learning framework, for the daily spatial interpolation of air temperature and precipitation, marking the first application of its localized encoder variant to the challenge of gridding climate data from a sparse station network. We investigate two distinct encoder configurations—Global and Localized—to determine the optimal structural prior for capturing spatial dependencies in data-scarce regimes. The models were developed and evaluated using data from a sparse network of meteorological stations in Romania from 2020 to 2023. To ensure applicability for long-term historical reconstruction, the input features were restricted to static topographic predictors derived from a Digital Elevation Model (DEM). Performance was benchmarked against Regression Kriging (RK), a standard geostatistical baseline that incorporates these same topographic covariates. Results demonstrate that the SMACNP architectures substantially outperform the RK baseline for both variables.

The SMACNP (Localized) configuration, which utilizes an attention mechanism, emerged as the most robust model, achieving the lowest Mean Absolute Error (MAE) and the highest correlation across the majority of seasons. The performance gains were particularly pronounced for precipitation, where the deep learning models effectively captured fine-scale spatial heterogeneity and non-linearities that traditional methods tended to over-smooth. These findings indicate that localized neural process-based models offer a powerful, scalable, and physically plausible alternative to geostatistical methods for generating high-quality gridded climate datasets in complex, data-sparse environments.

This research was supported by the project “Cross-sectoral Framework for Socio-Economic Resilience to Climate Change and Extreme Events in Europe (CROSSEU)” funded by the European Union Horizon Europe Programme, under Grant agreement n° 101081377.

How to cite: Dumitrescu, A.: A deep learning framework for gridding daily climate variables from a sparse station network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19697, https://doi.org/10.5194/egusphere-egu26-19697, 2026.

How to cite: Miranda, V., Castro, M., Paixão, J., Girão, I., Marques, B., Magnus Koktvedgaard Zeitzen, R., Cunha, R., Fonteneles, C., Pereira, É., Khudynian, M., Thejll, P., Jomo Danielsen Sørup, H., Paletta, Q., and Oliveira, A. P.: CLIM4cities - from Citizen Science, Machine Learning and Earth Observation towards Urban Climate Services , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19718, https://doi.org/10.5194/egusphere-egu26-19718, 2026.

The accurate assessment of extreme flood events and their associated losses requires massive sample sizes (e.g., 50,000+ years of weather data) for statistical robustness and a comprehensive coverage of event characteristics. Generating such a large dataset using dynamical Earth System Models would be extremely computationally intensive, so instead, we propose a lightweight and computationally efficient climate emulator built upon a video diffusion architecture. 

The model is trained to reproduce the statistical properties and physical dynamics of the Community Earth System Model version 2 (CESM2) over Europe. It operates autoregressively to generate synthetic, multivariate, daily atmospheric data (including temperature, specific humidity, wind vectors, and surface pressure) at ~100 km resolution. The model utilizes a U-Net architecture that is conditioned on previous time-steps to produce and evolve weather patterns with spatial and temporal consistency. To enhance the stability of long-term generation and improve the faithful reproduction of extremes, we employ a seasonality-aware standardization scheme, training the model to learn the dynamics in anomaly space rather than physical space.

We demonstrate that this approach successfully reproduces the complex spatiotemporal dependencies within CESM2, captures atmospheric dynamics, including the frequency and persistence of dominant circulation types, and can maintain stability over multi-decadal generation windows. Furthermore, the output of this emulator can be fed into existing downscaling models to produce higher resolution multivariate meteorological data fields to drive downstream impact models. We validate this full modeling chain by demonstrating that the resulting hydrological statistics exhibit physical characteristics consistent with the CESM2-driven benchmark.

This computationally efficient generative model offers a pathway to generating thousands of years of physically consistent flood events. 

How to cite: Marshall, A., Lucas, C., Addor, N., Lord, N., Moraga, J. S., Hoch, J., and Wing, O.: Fast emulation of climate models for precipitation and flood impact modelling using autoregressive video diffusion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19946, https://doi.org/10.5194/egusphere-egu26-19946, 2026.

Regional Climate Models (RCMs) are vital for capturing mesoscale variability, however remain too coarse for impact assessments in complex topographies like Switzerland. In this study, we bridge the "km-scale gap" by introducing a generative super resolution pipeline to downscale EURO-CORDEX ensemble to a 1 km grid over Switzerland.

We establish the added value of a deterministic residual U-Net, pixel-based as well as generative residual Latent Diffusion over operational baselines and conventional bias correction (BC) methods such as Cumulative Distribution Function - transform (CDF-t), Empirical Quantile Mapping (EQM) and dynamical Optimal Transport Correction (dOTC). Our results demonstrate that super resolved fields have superior distributional skill, better visual fidelity of fields, shows improved  trend preservation and representation of interannual variability across diverse biogeographical regions  and major population centres such as Bern, Zurich and Locarno. Further, as demonstrated by a marked reduction in bias for  20-, 50-, and 100-year return levels of multi-day precipitation totals, super resolution (SR) also complements BC for improved representation of extremes in our km-scale downscaled EUROCORDEX. Our findings establish that while BC methods remain essential for distributional fidelity, residual generative models offer a potent, actionable pathway for producing high-resolution climate information from coarse climate fields.

How to cite: Asthana, S., Koch, E., Kotlarski, S., and beucler, T.: Next-Generation Climate Projections: Insights from Blending Bias Correction with Super Resolution over Complex Terrain, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4507, https://doi.org/10.5194/egusphere-egu26-4507, 2026.

Cloud feedback is one of the key sources of uncertainty in the sensitivity of climate projections to anthropogenic forcing in Earth system models (ESMs). Improving its representation remains challenging because clouds sit at the intersection of radiation, dynamics, and microphysics, and small errors in any of these can strongly affect climate sensitivity.. Consequently, analysing and understanding errors in simulated cloud feedback, evaluated against observations, is essential for advancing cloud parameterizations in ESMs.

In this work, we explore methodological frameworks for evaluating cloud feedback in climate models that move beyond simple model–observation comparisons toward physically interpretable insights into model properties and dynamics. We propose two advances: (1) improved cloud regime identification by extending standard k-means clustering to Wasserstein k-means, and (2) the use of explainable machine-learning methods to evaluate the extent the ESMs capture the realistic sensitivity between the cloud radiative anomalies and key cloud-controlling factors. We demonstrate these approaches by evaluating different versions of the HadGEM model in AMIP experiments against observations, illustrating their potential to support more physically grounded diagnosis of cloud-feedback behaviour in climate models.

How to cite: Mankovich, N., Gavrilov, A., Huang, F., Camps-Valls, G., Lan, F., and Bodas-Salcedo, A.: Explainable Cloud Feedback Evaluation in Earth System Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13326, https://doi.org/10.5194/egusphere-egu26-13326, 2026.

Exploring uncertainty and internal variability across future emission pathways remains computationally demanding with state-of-the-art Earth system models (ESMs). We present a diffusion-based machine-learning emulator trained on output from the CESM2 large ensemble dataset to reproduce absolute annual-mean temperature and year to year variability,  conditioned on anthropogenic co2 and sulfate emisisson from ssp3-7.0 scenario. The emulator employs a three-dimensional UNet architecture that learns the spatiotemporal distribution of global temperature fields in latitude–longitude–time space. Conditioning variables include cumulative CO₂ and aerosol emissions, enabling the generation of physically consistent climate responses under arbitrary emission trajectories.To enhance physical interpretability, we integrate explainable AI (XAI) methods, including gradient-based attribution and sensitivity analyses, to quantify how emission-related conditioning variables influence regional temperature responses. The emulator reduces computational cost by several orders of magnitude compared to full ESM simulations, enabling rapid scenario exploration and uncertainty assessment. This framework aims provides a scalable and interpretable pathway for fast climate response emulation

How to cite: Nordling, K.: Emulating absolute annual temperatures and variability  from the CESM2 Large Ensemble using a diffusion model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2500, https://doi.org/10.5194/egusphere-egu26-2500, 2026.

Statistical causality methods are becoming increasingly widespread in climate teleconnection analysis, but they typically require a prior reduction of high-dimensional, multivariate climate fields. Most common aggregation techniques, such as spatial averaging or Principal Component Analysis (PCA) (largely known as Empirical Orthogonal Functions, EOF, in the climate community) [1], are not designed to preserve causal structure and can mask spatially complex or low-variance causal signals.

We introduce Granger PCA [2], a novel dimensionality reduction method that explicitly extracts components that are influenced by a causal driver. Instead of maximizing variance, Granger PCA identifies spatial patterns whose associated time series are maximally Granger caused by an external variable, such as a large-scale climate mode. This is achieved by optimizing spatial weights to maximize the Granger causality F-statistic and yields a low-dimensional representation that captures the Granger causal information present in the field.

The method is particularly effective in cases where causal effects are spatially heterogeneous, have low variance, or are hidden by strong local autocorrelation. In such cases, variance-based methods can fail even when robust causal influence exists.

We apply Granger PCA to several teleconnection problems, including the influence of the North Atlantic Oscillation on precipitation and the impact of ENSO on vegetation variability. Granger PCA recovers physically interpretable patterns that are not captured by PCA or correlation-based approaches.

In summary, Granger PCA provides a simple and interpretable framework for causally oriented dimensionality reduction and offers a new tool for teleconnection analysis in climate science.

References

• [1] A. Hannachi, I. T. Jolliffe, D. B. Stephenson et al., “Empirical orthogonal functions and related techniques in atmospheric science: A review,” International Journal of Climatology, vol. 27, no. 9, pp. 1119–1152, 2007.
• [2] G. Varando, M.-Á. Fernández-Torres, J. Muñoz-Marí, and G. Camps-Valls, “Learning causal representations with Granger PCA,” in UAI 2022 Workshop on Causal Representation Learning, 2022.

How to cite: Durand, H., Varando, G., and Camps-Valls, G.: Granger PCA: Extracting Granger-causal patterns in climate fields, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10055, https://doi.org/10.5194/egusphere-egu26-10055, 2026.

Understanding long-term trends in stratospheric species is vital for evaluating the success of the Montreal Protocol and its amendments. However, reliable trend estimation remains challenging due to the sparse spatial and temporal coverage of high-quality observations, such as those from the Atmospheric Chemistry Experiment–Fourier Transform Spectrometer (ACE-FTS).

How to cite: Dhomse, S. and Chipperfield, M.:   Machine Learning For Atmospheric Chemistry: Creating Global, Gap-Free Stratospheric Datasets for Montreal Protocol Assessments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4514, https://doi.org/10.5194/egusphere-egu26-4514, 2026.

Generating high-resolution (HR) weather and climate information at ~10 km or finer across the Himalayan regions remains a major challenge due to extremely high computational cost of forecasting models and complexity of atmospheric processes. Most operational global weather prediction systems run at low-resolution (LR) of ~25 km or coarser, that are inadequate for impact-based analyses of highly localized extreme weather events common to these regions. To bridge this gap, downscaling is essential for producing climate information at impact-relevant scales, with both statistical and dynamical approaches remaining widely used despite major shortcomings. The former is computationally efficient but often fail under future climate non-stationarity, while the latter, though physically consistent, is computationally expensive and constrained by domain-resolution trade-offs. Currently, there is no efficient data-driven approach that can produce regional-model-scale precipitation fields for the Himalayan region. This work presents WGAN, a deterministic deep neural generative adversarial network (GAN)-based emulator of the Weather Research and Forecasting (WRF) model for HR precipitation downscaling over the Himalayan region. The model is conditioned on LR meteorological variables from the European Centre for Medium-Range Weather Forecasts Re-Analysis version 5 (ERA5; 0.25°×0.25°) as input and is trained against HR precipitation from WRF (0.1°×0.1°), which uses ERA5 as boundary conditions. The architecture uses Wasserstein-1 distance (WGAN) in the generator and critic value functions with a gradient penalty for stable training. WGAN demonstrated the ability to generate fine-scale precipitation fields that closely matches WRF’s outputs, accurately capturing spatial patterns and the mean values. Incorporating terrain and an extreme aware-weighting MSE (Mean Squared Error) loss function in the model further improves precipitation magnitude representation, reduces biases, and yield ~29% reduction in RMSE in the upper decile. The model effectively captured low-frequency (large-scale) variability and better matches WRF’s power spectrum at mid-high frequency (short-scale) variability. This raises the probability of detection and lowers the false alarm rate across thresholds. With a case study, WGAN showed the ability to capture the fine-scale spatial distribution of precipitation in the mountains and foothills, at both extreme precipitation day and dry conditions, outperforming CNN-based precipitation output. These results underscore the capability of WGAN as a fast and efficient tool for precipitation downscaling for the Himalayan region, operating at only a fraction of the computational cost. The model has strong potential for operational use in early warning, risk assessment, vulnerability analysis, disaster management, and other sectors that rely on localized climate information, ultimately supporting the preparedness of communities living in and around these mountains.

How to cite: Lyngwa, R., Nikumbh, A., and Ghosh, S.: A Generative-driven Model for Precipitation Downscaling Over Himalayan Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-821, https://doi.org/10.5194/egusphere-egu26-821, 2026.

Earth system model (ESM) intercomparison is essential for assessing model performance and identifying future challenges in climate modeling. The Taylor diagram [1] is one of the most widely used tools for this purpose, as it provides an intuitive summary of standard evaluation metrics — such as correlation, root-mean-square error, and standard deviation — by comparing multiple simulated datasets against a reference, typically observations or a ground truth, within a single plot.

However, in several relevant applications, including the development of new ESM parameterizations, the comparison of conceptual models, or the evaluation of simulated statistical distributions, classic linear correlation and RMSE metrics may be insufficient. Here, we propose a set of extensions to the Taylor diagram based on a generalization of cross-covariance using kernels, allowing both nonlinear relationships and distributional aspects of similarity to be taken into account. Nonlinear similarity is characterized through a kernel-space analogue of rotational alignment, while distributional similarity can be quantified using metrics such as maximum mean discrepancy, as originally introduced in [2], as well as alternative kernel-based measures. Using controlled synthetic experiments, we show that the proposed kernel Taylor diagrams can resolve differences in model skill that remain indistinguishable under the classical Taylor diagram. These results indicate that the kernel-based extensions provide complementary diagnostic information to standard metrics and can support more informative Earth system model evaluation and development.

[1] Taylor, K. E. (2001), Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106(D7), 7183–7192, doi:10.1029/2000JD900719.

[2] Wickstrøm, K., Johnson, J. E., Løkse, S., Camps-Valls, G., Mikalsen, K. Ø., Kampffmeyer, M., & Jenssen, R. (2022). The Kernelized Taylor Diagram. doi:10.48550/arXiv.2205.08864

How to cite: Gavrilov, A., Mankovich, N., Link, M., Huang, F., and Camps-Valls, G.: Kernel Taylor Diagram for Earth System Model Evaluation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7678, https://doi.org/10.5194/egusphere-egu26-7678, 2026.