The Rise of AI in Weather and Climate Information and its Impact on Global Inequality

Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race

arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.

Introducing WeatherNext 3, our most advanced and accurate global weather AI model

A Checklist to assess the energy and carbon impacts of ML/AI applications in Earth System Modeling

arXiv:2609.00847v1 Announce Type: cross Abstract: As machine learning and artificial intelligence find their way into nearly every aspect of climate, weather, and Earth system modeling, it is worth p...

From Manual Construction to AI-Driven Scenario Emergence: Rethinking Catastrophe Risk Modeling

The article introduces the TAISE framework, which uses AI weather forecasting models to generate coherent extreme weather sequences at a fraction of the cost of traditional catastrophe risk models. By self‑iteratively producing continuous global atmospheric fields, TAISE captures temporal continuity and cross‑regional correlations that snapshot‑based methods miss. A proof‑of‑concept experiment shows an order‑of‑magnitude reduction in computational cost while maintaining key statistical properties of extreme events.

Capturing Unseen Spatial Heat Extremes Through Dependence-Aware Generative Modeling

The paper introduces DeepX-GAN, a deep generative model that captures spatial dependence in rare climate extremes. It can simulate statistically plausible unseen heat extremes—both direct-hit and near-miss events—beyond the observed record. Applied to the Middle East and North Africa, the model shows that unseen heat extremes disproportionately affect vulnerable countries and that future warming could create new persistent hotspots, underscoring the need for spatially adaptive resilience planning.

Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction

arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.

Learning Hierarchical Causal Representations of the Effects of Forcings on Temperature in Climate Models

The paper presents a hierarchical causal representation learning framework that models both internal climate variability and forced responses in sea surface temperature fields from a global climate model. By training on future climate change scenarios, the method accurately predicts long‑term global mean and regional temperature evolution and reproduces realistic responses to perturbations in greenhouse gas and aerosol concentrations on unseen scenarios. This demonstrates the potential of causal representation learning to improve climate model emulation.

Our contribution to a global environmental standard for AI

WeatherNext 2: Our most advanced weather forecasting model

The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions.

Improving global precipitation forecasts with an AI weather model trained on satellite observations

The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.

Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation

arXiv:2603. 22320v2 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.

Advancing Subseasonal Forecasting with Machine Learning

The paper introduces Probabilistic Bias Correction (PBC), a machine learning framework that learns to correct historical probabilistic forecasts, thereby reducing systematic errors in subseasonal weather predictions. Applied to leading dynamical and AI models from ECMWF, PBC doubles the AI system’s modest subseasonal skill and improves the operationally-debiased dynamical model for most pressure, temperature, and precipitation targets. In ECMWF’s 2025 real‑time forecasting competition, PBC’s global forecasts ranked first across all weather variables and lead times, outperforming multiple operational and ensemble models.