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.

extreme precipitation,early warning,total energy