Findings from published research, checked in the open

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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Keyword: Fuxi ×Clear all

7 claims from 4 papers

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Physics-based models outperform AI weather forecasts of record-breaking extremes

   Zhang, Fischer, Zscheischler and Engelke · Science Advances · 2026

   Unchecked3 claims
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   1. Unchecked“Here, we show that for record-breaking weather extremes, the physics-based numerical model High RESolution forecast (HRES) from the European Centre for Medium-Range Weather Forecasts still consistently outperforms state-of-the-art AI models GraphCast, GraphC…
   2. Unchecked“We demonstrate that forecast errors in AI models are consistently larger for record-breaking heat, cold, and wind than in HRES across nearly all lead times.”
   3. Unchecked“We further find that the examined AI models tend to underestimate both the frequency and intensity of record-breaking events, and they underpredict hot records and overestimate cold records with growing errors for larger record exceedance.”

2. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Data-driven forecasts of extreme weather in East Asia: feasibility of operational use

   Oh, Bae, Son et al. · Weather and Climate Extremes · 2026

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   1. Unchecked“Overall forecast skills increase when MLWP models are initialized with ERA5 reanalysis, highlighting the importance of initial conditions even in MLWP.”

3. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   An extension of WeatherBench 2 to binary hydroclimatic forecasts

   Zhao, Li, Tu and Chen · Geoscientific model development · 2025

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   1. Unchecked“For wet extremes, the GraphCast tends to outperform the IFS HRES when using the total precipitation of ERA5 reanalysis data as the ground truth.”
   2. Unchecked“For warm extremes, Pangu-Weather, GraphCast and FuXi tend to be more skillful than the IFS HRES within 3 d lead time but become less skillful as lead time increases.”

4. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Community Research Earth Digital Intelligence Twin: a scalable framework for AI-driven Earth System Modeling

   Schreck, Sha, Chapman et al. · npj Climate and Atmospheric Science · 2025

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   1. Unchecked“Our findings show that both FUXI and WXFormer, trained on six-hourly ERA5 hybrid sigma-pressure levels, generally outperform IFS HRES in 10-day forecasts, offering potential improvements in efficiency and forecast accuracy.”