Emerging energy signals advance early warnings of extreme precipitation

The increasing frequency and intensity of extreme precipitation events pose growing societal risks, underscoring the urgent need for effective early warning systems. Forecasting such events at subseasonal-to-seasonal timescales, however, remains a major scientific challenge. Utilizing Seasonal Forecast System version 5 from the European Centre for Medium-Range Weather Forecasts, here we show that introducing a total atmospheric energy proxy into forecasts substantially improves extreme precipitation prediction at subseasonal and longer timescales. This approach extends the effective forecast lead times from about one month by conventional methods to six months, and achieves more than tenfold increases in global prediction accuracy over the past two decades. The added predictive value of total energy has increased substantially in recent years, reflecting a strengthening relationship between total energy and extreme precipitation. These findings highlight the emerging energy signals that enhance predictability, offering promising opportunities for developing more reliable early warning systems in a warming future.

This study identifies three key advantages of introducing atmospheric total energy as a proxy for early warnings of extreme precipitation: (1) A substantial improvement in prediction skill at subseasonal and longer timescales. Incorporating total energy into forecast significantly enhances the accuracy of extreme precipitation prediction across regional and global scales. Compared with direct precipitation forecasts, total energy-based predictions yield up to a tenfold increase in hit rates for extreme precipitation days globally; (2) Increasing predictive value in recent years. The added skill from total energy has grown markedly since the 2010s, reflecting both a strengthened relationship between total energy and precipitation extremes and rising total energy levels under climate warming. The growing spatial coherence and intensity of large-scale extreme precipitation further amplify this signal; (3) Physical interpretability through energy budget analysis. A diagnosis of the total energy budget offers a physically grounded framework for identifying sources of predictability and model error, thus providing guidance for detecting the forecast limit and improving forecast systems.