# FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere

arXiv: https://arxiv.org/abs/2411.10191 · HTML: https://mlsanity.com/paper/2411.10191/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [FuXi: A cascade machine learning forecasting system for 15-day global weather forecast](https://mlsanity.com/paper/2306.12873/) (2023)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [FengWu: Pushing the Skillful Global Medium-range Weather Forecast beyond 10 Days Lead](https://mlsanity.com/paper/2304.02948/) (2023)
- [Sub-seasonal forecasting with a large ensemble of deep-learning weather prediction models](https://mlsanity.com/paper/2102.05107/) (2021)
- [Will Artificial Intelligence supersede Earth System and Climate Models?](https://mlsanity.com/paper/2101.09126/) (2021)
- [Coupled Ocean-Atmosphere Dynamics in a Machine Learning Earth System Model](https://mlsanity.com/paper/2406.08632/) (2024)
- [Data-driven Global Ocean Modeling for Seasonal to Decadal Prediction](https://mlsanity.com/paper/2405.15412/) (2024)
- [Robustness of AI-based weather forecasts in a changing climate](https://mlsanity.com/paper/2409.18529/) (2024)
- [Improving Global Weather and Ocean Wave Forecast with Large Artificial Intelligence Models](https://mlsanity.com/paper/2401.16669/) (2024)

## Built on by (most cited first, 8 of 8)

- [A Definition and Roadmap for World Models](https://mlsanity.com/paper/2607.06401/) (2026)
- [LUCIE-3D: A three-dimensional climate emulator for forced responses](https://mlsanity.com/paper/2509.02061/) (2025)
- [A machine learning model for skillful climate system prediction](https://mlsanity.com/paper/2505.06269/) (2025)
- [Earth-o1: A Grid-free Observation-native Atmospheric World Model](https://mlsanity.com/paper/2605.06337/) (2026)
- [AI-informed model-analogs for understanding subseasonal-to-seasonal jet stream and North American temperature predictability](https://mlsanity.com/paper/2506.14022/) (2025)
- [Data-driven global ocean model resolving ocean-atmosphere coupling dynamics](https://mlsanity.com/paper/2508.10908/) (2025)
- [Skillful Subseasonal-to-Seasonal Forecasting of Extreme Events with a Multi-Sphere Coupled Probabilistic Model](https://mlsanity.com/paper/2512.12545/) (2025)
- [Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms](https://mlsanity.com/paper/2607.05101/) (2026)

## Abstract (arXiv)

Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate forecasts. To explore the seamless forecasting ability based on one AI model, we propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. FengWu-W2S can generate 6-hourly atmosphere forecasts extending up to 42 days through an autoregressive and seamless manner. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO). Moreover, our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential pathways for developing AI-based integrated system for seamless weather-climate forecasting in the future.