Predictability of temperature extremes in multi-annual forecasts

Abstract Temperature extreme events, associated with major impacts on various socioeconomic sectors, exhibit trends related to global warming and undergo important variability across different timescales. While attention has been given to seasonal and decadal predictions, there is growing interest in exploring the potential for skillful predictions also at interannual timescales. In the current study, we assess the skill of the Community Earth System Model-Seasonal-to-Multiyear Large Ensemble in predicting temperature extremes, globally and in all calendar seasons, up to two years ahead. This ensemble of 24 month-long hindcasts enables a comprehensive assessment of interannual predictability, since it is initialized quarterly per year. The study evaluates the capability of the prediction system to forecast the number of days in episodes of extreme temperature anomalies, considering such anomalies in all calendar seasons and studying each forecast season independently. In general, significant predictive skill is found over many regions, depending on the calendar season. As expected, the skill is higher in the first forecast season and generally decreases over time. However, notable skill persists in some regions even up to forecast-season seven. Importantly, in certain regions, significant skill remains up to approximately forecast-season four, even after removing the externally forced signal as estimated from the corresponding uninitialized historical simulations. This suggests that in certain areas, internal variability of the coupled ocean–land–atmosphere system contributes to the predictability of temperature extremes even beyond the seasonal timescale. The role of El Niño–Southern Oscillation as a source of predictability is also assessed and is found to contribute significantly, especially for the boreal winter (DJF) and spring (MAM) up to forecast-season four. However, there is evidence that additional sources of predictability may contribute.