Fig. 1: Comparison of globally-averaged and latitude-weighted temporal anomaly correlation coefficient (TCC) of the ensemble mean between ECMWF subseasonal-to-seasonal (S2S) reforecasts (in blue) and FuXi-S2S forecasts (in red) for total precipitation (TP), 2 m temperature (T2M), geopotential at 500 hPa (Z500), and outgoing longwave radiation (OLR). | Nature Communications

Fig. 1: Comparison of globally-averaged and latitude-weighted temporal anomaly correlation coefficient (TCC) of the ensemble mean between ECMWF subseasonal-to-seasonal (S2S) reforecasts (in blue) and FuXi-S2S forecasts (in red) for total precipitation (TP), 2 m temperature (T2M), geopotential at 500 hPa (Z500), and outgoing longwave radiation (OLR).

From: A machine learning model that outperforms conventional global subseasonal forecast models

Fig. 1

Rows 1 and 2 represent the performance across these variables, utilizing all testing data from the period spanning from 2017 to 2021. A bootstrapping approach, repeated 1000 times, is used for significance testing. When the FuXi-S2S forecasts fail to show a statistically significant improvement over the ECMWF S2S reforecasts at the 97.5% confidence level, a pale color scheme is used to denote these results.

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