The predictability of daily temperature and precipitation extremes is assessed out to a decade ahead using the Met Office Decadal Prediction System. Extremes are defined using a simple percentile based counting method applied to daily gridded observation data sets and corresponding model forecasts. We investigate moderate extremes, with a 10% probability of occurrence, ensuring they are frequent enough for robust skill analysis while having sizable impacts. We quantify the predictability of extremes, assess the impact of initialization, and compare with the predictability of the mean climate. We find modest but significant skill for seasonal predictions of temperature extremes in most regions (global area-average correlation of 0.3) and for precipitation extremes over the USA (area-average correlation of 0.2). The skill of both temperature and European rainfall extremes improves for multiyear forecast periods, as longer averaging periods reduce the impact of unpredictable short-term variations, capitalizing on predictable trends from external forcings. For 5 year periods out to a decade ahead, root-mean square temperature errors are reduced by 20% compared to use of climatology in most regions, apart from the southeastern USA. Initialization improves forecast skill for temperature and precipitation extremes on seasonal timescales in most regions. However, there is little improvement beyond the first year suggesting that skill then arises largely from external forcings. The skill for extremes is generally similar to, but slightly lower than, that for the mean. However, extremes can be more skillful than the mean, for example, USA cold nights, where trends in extremes are greater than for the mean.
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