Sample sizes of observed climate extremes are typically too small to reliably constrain return period estimates when there is non-stationary behaviour. To increase the historical record 100-fold, we apply the UNprecedented Simulated Extreme ENsemble (UNSEEN) approach, by pooling ensemble members and lead times from the ECMWF seasonal prediction system SEAS5. We fit the GEV distribution to the UNSEEN ensemble with a time covariate to facilitate detection of changes in 100-year precipitation values over a period of 35 years (1981–2015). Applying UNSEEN trends to 3-day precipitation extremes over Western Norway substantially reduces uncertainties compared to estimates based on the observed record and returns no significant linear trend over time. For Svalbard, UNSEEN trends suggests there is a significant rise in precipitation extremes, such that the 100-year event estimated in 1981 occurs with a return period of around 40 years in 2015. We propose a suite of methods to evaluate UNSEEN and highlight paths for further developing UNSEEN trends to investigate non-stationarities in climate extremes.

当存在非平稳行为时，观测到的极端气候的样本量通常太小而无法可靠地限制返回期的估算。为了将历史记录增加100倍，我们通过合并ECMWF季节预测系统SEAS5中的合奏成员和提前期，应用了前所未有的模拟极端合奏（UNSEEN）方法。我们将GEV分布与时间协变量拟合到UNSEEN集合中，以方便检测35年（1981-2015年）内100年降水值的变化。与基于观测记录的估计相比，将UNSEEN趋势应用于挪威西部3天的极端降水量可以大大减少不确定性，并且不会随时间返回明显的线性趋势。对于斯瓦尔巴特群岛，UNSEEN趋势表明极端降水量显着上升，