ArticleNature communications2024
A machine learning model that outperforms conventional global subseasonal forecast models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Predicting regional gray swans via translocation: AI weather models and Dubai's unprecedented 2024 rainfall.Science advances · 2026Article
- Bridging the weather and climate divide with artificial intelligence.Nature communications · 2026Review
- Data-driven global ocean model resolving atmospherically forced ocean dynamics.Science advances · 2026Article
- Emerging energy signals advance early warnings of extreme precipitation.Nature communications · 2026Article
- Reconstructing fine-scale 3D wind fields with terrain-informed machine learning.Nature communications · 2026Article
- AI for atmosphere-ocean sciences: advancements, challenges and ways forward.National science review · 2026Review
- Seasonal forecasting using the GenCast probabilistic machine learning model.Climate dynamics · 2026Article
- Mobility as climate change adaptation in South Africa: Exploring the legal and policy significance of artificial intelligence.Jamba (Potchefstroom, South Africa) · 2026Article
- Delayformer: Spatiotemporal Transformation for Predicting High-Dimensional Dynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The application of large language models in meteorology graduate research: current status, impact, and prospects.PloS one · 2026Article
- A machine learning model for hub-height short-term wind speed prediction.Nature communications · 2025Article
- A global estimate of multiecosystem photosynthesis losses under microplastic pollution.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enhancing fever of unknown origin diagnosis: machine learning approaches to predict metagenomic next-generation sequencing positivity.Frontiers in cellular and infection microbiology · 2025Article
- A machine learning model that outperforms conventional global subseasonal forecast models.Nature communications · 2024Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.