DeepMind Blog
Nov 17, 2025
WeatherNext 2: Our most advanced weather forecasting model
The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions.
Improving precipitation forecasts in an AI weather model using observational data
The paper presents a graph-transformer AI weather model that is fine‑tuned with high‑resolution IMERG precipitation observations, moving beyond the traditional reliance on the ERA5 reanalysis dataset. This approach yields up to a 19% improvement in medium‑range continuous ranked probability scores and a 57% better Brier skill score for extreme rainfall compared to leading operational models, while also excelling in tropical storm and drizzle prediction. The study demonstrates that directly incorporating observation‑based precipitation data into AI training can markedly enhance forecast accuracy, though physics‑based models still outperform for the heaviest events.
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arXiv Machine Learning
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CSU-PCAST: A Dual-Branch Transformer Framework for medium-range ensemble Precipitation Forecasting
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arXiv AI
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PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast
PCSDiff is a diffusion-based framework designed to correct systematic biases and enhance spatial resolution in medium-term (10‑day) precipitation forecasts. It uses a Precipitation Intensity‑aware Multi‑branch Decoder to mitigate dynamic multi‑day errors and a two‑phase conditional diffusion super‑resolution module to restore fine‑scale rainfall patterns. Evaluated over China, PCSDiff reduces RMSE by 16.1% and increases ACC by 13.9% compared to raw ECMWF forecasts, outperforming mainstream deep‑learning baselines and enabling low‑latency rolling forecasts for operational use.
arXiv Machine Learning
Sep 7
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.
arXiv Machine Learning
Sep 2
GenONet: A Generative operator Network for High-Resolution Precipitation Nowcasting
GenONet introduces a Spatio-Temporal U-DeepONet architecture that serves as a generator in a GAN framework for high‑resolution precipitation nowcasting up to three hours ahead. By learning continuous‑time precipitation dynamics with a Deep Operator Network and enforcing physics through a moisture‑conservation loss, the model produces sharp, physically consistent forecasts that outperform baselines, especially for high‑intensity events and longer lead times. Ablation studies confirm the added value of the physics‑informed regularizer and the synergy of operator learning with adversarial training.
arXiv Machine Learning
Jun 26
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
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arXiv Statistics ML
2d ago
High-resolution Calibrated Probabilistic Hourly Precipitation from a Deterministic Forecast
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arXiv Machine Learning
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SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
arXiv:2605. 16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost.
arXiv Machine Learning
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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.
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Hugging Face Trending Papers
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Weather Emulators at the Frontier of Heat Extremes Predictability
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet in a warming world, improving early warnings of extreme heat is an increasingly critical challenge.