- ID5: WRF model and Meteosat with Machine Learning for Forecast of Low Stratus and Fog Tested on a Special Event (Dorita Rostkier-Edelstein – Holon Institute of Technology)
- ID6: Evaluating the Potential of Foundation Models for Land Use and Land Cover Classification (Magali Egger – Université de Lausanne)
- ID8: Physics-informed neural networks predict changes in terrestrial water storage and sea levels better than climate models (Mostafa Kiani Shahvandi – University of Vienna)
- ID11: Evaluating Finetuned Foundation Models for Extreme-Event Representation: Insights from Atmospheric Rivers (Noelia Otero – Fraunhofer HHI)
- ID12: Linking BSISO Variability, Extreme Rainfall in the Present and the Future (Aditya Kottapalli – Indian Institute of Science)
- ID16: Compounding hazards increase flood economic losses across Europe (Alois Tilloy – Joint Research Centre of the European Commission)
- ID18: Efficiency of Machine Learning Methods for Extreme Precipitation Analysis (Nirajan Dhakal – Spelman College)
- ID21: A Trajectory-Based Diagnostic Framework for Convective Organization: A Data-Driven Approach to Monsoon Rainfall Processes (Moufeng Wan – Hong Kong University of Science and Technology)
- ID22: Evaluating Spatial Generalisation of Generative Downscaling (Maybritt Schillinger – ETH Zurich)
- ID23: ClimaGuard: A Transparent AI Framework for Assessing Solar Radiation Management (Philine Bommer – TU Berlin)
- ID24: Can generative AI models downscale very rare events? A study of the 2020 south of France flash flood. (Pierre Chapel – LMD-IPSL)
- ID25: Unifying context-specific causal discovery in the PCMCI-framework with applications to river catchment data (Wiebke Günther – Technical University Berlin)
- ID28: Projected changes in the Arctic sea-ice seasonal cycle inferred from clustering (Perrine Bauchot; Lab-STICC)
- ID29: Coupling Machine Learning and Data Assimilation for Ocean Eddy Parameters’ Prediction (Solène Dealbera – IMT Atlantique)
- ID31: Machine Learning Downscaling for Wind and Solar Energy Drought Duration (Nina Effenberger – ETH Zürich)
- ID32: How can climate model emulators be aligned more closely with the needs of applied researchers? (Luca Schmidt – University of Tübingen)
- ID34: Attribution of Pacific trend discrepancies using the Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP) (Robert Jnglin Wills – ETH Zurich)
- ID37: Diffusion Models for Generative Emulation of Regional Climate Models: Simulating Downscaling Uncertainty (Mikel Legasa – LSCE-IPSL)
- ID42: Learning Spatiotemporal Precipitation Fields with Probabilistic Neural Processes (Pritthijit Nath – University Of Cambridge)
- ID44: Spatially-contiguous reconstruction of water temperature and discharge in Switzerland using deep learning (Louis Poulain–Auzéau – ETH Zürich)
- ID48: Sim2Real Conditional Diffusion for Radar Beam Blockage Correction: A Preliminary Study (Assaad Zeghina – Latmos Lab)
- ID51: Simulating riverine heatwaves: a European reconstruction of river temperatures with an LSTM (Corinna Frank – ETH Zurich)
- ID52: Machine Learning Estimation of Arctic Sea Ice Thickness Distribution in Coarse-Resolution Ocean Models Trained with High-Resolution Satellite Data (Léo Edel – NERSC)
- ID57: Evaluation of Deep Learning Models for Satellite IR Precipitation Estimation (Matthieu Meignin – LATMOS/UVSQ)
- ID61: Adaptive regionalization for extreme precipitation: A neural network-weighted independence likelihood approach (Robert Paulus – UCLouvain)
- ID62: Topography-aware Temperature Forecasting in Europe (
Chang Xu – EPFL) - ID65: Equivariance-based self-supervised learning and SAR tomography for monitoring forest structures (Zoe Berenger – Telecom Paris)
- ID70: Charting the unseen – A journey into disaster displacement risk (Maxime Souvignet – UNU-EHS)
- ID72: End-to-end learning from simulated observations for the neural mapping of real altimetry data (Daniel Zhu – IMT Atlantique)
- ID75: Statistical downscaling for precipitation examinating potential temporal dependence due to climate change (Weixi Sun – Centralesupelec, L2S Laboratory)
- ID76: Integrating a ML-enhanced Radiation Parameterization into ICON-XPP (Katharina Hafner – University of Bremen)
- ID77: A Machine Learning approach to study the ocean-atmosphere interactions in CESM2 for the North Atlantic (Clara Wetzel – EPFL)
- ID78: Evaluating ArchesWeather and ArchesWeatherGen under Multi-Decadal AMIP-style climate simulations (Renu Singh – Google Deepmind / INRIA France)
- ID80: Hybrid dynamical-statistical modelling of the North Atlantic climate (Elena Provenzano – LOCEAN (IPSL))
- ID89: Tracks for Enhancing Rainfall-Induced Landslide Prediction (Jacques Soutter – UNIL)
- ID90: SVD-ROM: Scalable Reduced-Order Modeling of Weather and Climate Data Using the Singular Value Decomposition (David Salvador-Jasin – The Alan Turing Institute)
- ID93: Towards a distributional autoencoder for climate counterfactuals (Frieder Loer – Institute for Meteorology, Leipzig University)
- ID98: GOES-ABI Data Cube: Unifying Eight Years of Geostationary Satellite Observations (Mickell Als – University of Toronto)
- ID108: Seeing the Air: Social Media Imagery for Seasonal Air Quality Monitoring in a Himalayan Valley City (Bhavya S – TERI SAS)
Climate Informatics 2026