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Predictability of large-scale extreme droughts from global bias-corrected seasonal forecasts
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DOI：10.3389/frwa.2026.1911791.png)
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Abstract
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Extreme droughts are increasing in severity and frequency due to climate change; highlighting the need for reliable early-warning information at seasonal time scales. Bias-corrected seasonal forecasts offer a promising basis for anticipating drought conditions several months in advance; yet their predictive skill and potential operational value for the most severe droughts at the global scale remains insufficiently quantified. Here; we assess the performance of seasonal drought forecasts derived from the SEAS5-BCSD dataset using the Standardized Precipitation Evapotranspiration Index (SPEI). Thirty-six of the most extreme drought events between 1981 and 2024 are identified from ERA5 reanalysis; selecting two events per continent and accumulation period (SPEI-1; SPEI-3; and SPEI-6); and assessing forecast performance using probabilistic; spatial; and impact-oriented verification metrics; including the Continuous Ranked Probability Skill Score (CRPSS); the Fractions Skill Score (FSS); and the Potential Economic Value (PEV). The results demonstrate that SEAS5-BCSD drought forecasts outperform climatology for nearly all analyzed events. Across all verification metrics; forecast skill is generally highest for SPEI-1 events and for the selected African droughts; whereas the selected North American events exhibit comparatively lower skill. While the exact location and intensity of the most severe drought cores remain difficult to predict; useful probabilistic information is often preserved within the ensemble distribution. Maximum PEV values frequently exceed 0.7; indicating substantial potential value for decision-making under suitable cost-loss assumptions. These findings highlight the benefits of probabilistic; ensemble-based interpretation of seasonal drought forecasts. Rather than relying solely on the ensemble mean; considering the full forecast distribution enables more risk-informed drought preparedness by accounting for both the likelihood and the potential severity of extreme events.
Keywords:
drought
SPEI
decision support
seasonal forecast
SEAS5
bias correction and spatial disaggregation
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360
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