Trends inflate estimates of subseasonal skill for surface temperatures

Subseasonal-to-seasonal (S2S) predictions have numerous applications and improving forecast skill on this time scale has become a major effort. Since forecast uncertainty is high on S2S lead times, ensemble prediction systems are essential in order to provide probabilistic forecasts, informing about the range of possible outcomes. For evaluating their performance, these forecasts are routinely compared to a climatological reference forecast. The climatological distribution is commonly assumed to be stationary over the verification period. However, prominent deviations from this assumption exist, especially considering trends associated with climate change. Using synthetic forecast-verification pairs we show that estimates of the probabilistic skill of both continuous and categorical forecasts with a fixed actual level of skill increase as a function of the variance explained by the trend over the hindcast period. The skill of categorical forecasts can be inflated even further when evaluated over a longer forecast period. We also show that this skill enhancement can be observed in the ECMWF extended-range ensemble prediction system. We demonstrate that the effects on the skill in an operational forecast setting are currently strongest in the tropics and mainly relevant for categorical forecasts. This highlights that care needs to be taken when evaluating forecasts that are subject to non-stationarity on time scales much longer than the forecast verification window, especially for categorical forecasts. The results presented in this study are by no means limited to the S2S time scale but have similar implications for the verification of seasonal to decadal predictions, where the existence of trends can further inflate forecast skill.