Fig. 2: Maps displaying the average Ranked Probability skill Score (RPSS) (first and second rows) and Brier Skill Score (BSS) (third and fourth rows) without latitude weighting, comparing ECMWF subseasonal-to-seasonal (S2S) (first column) and FuXi-S2S (second column) forecasts.
From: A machine learning model that outperforms conventional global subseasonal forecast models
Additionally, the third column depicts the difference in RPSS and BSS between FuXi-S2S and ECMWF S2S for total precipitation (TP) at forecast lead times of weeks 3–4 (first and third rows) and weeks 5–6 (second and fourth rows), utilizing all testing data from 2017 to 2021. Red contour lines in the first and second columns indicate areas with positive values of RPSS and BSS. Stippling on the map denotes areas where the skill score is statistically significant at the 97.5% confidence level. Specifically, in columns 1 and 2, stippling indicates regions where the skill scores of the ECMWF S2S and FuXi-S2S models significantly surpass those of climatology. In column 3, stippling highlights areas where the FuXi-S2S model significantly outperforms the ECMWF S2S.
