The importance of context in extreme value analysis with application to extreme temperatures in the U.S. and Greenland
研究了在气候变化背景下，如何通过纳入科学背景（如随机效应和高斯混合模型）改进极端温度事件的统计建模，提高预测准确性，对气候风险评估和适应政策制定有参考价值。
Abstract Statistical extreme value models allow estimation of the frequency, magnitude, and spatio-temporal extent of extreme temperature events in the presence of climate change. Unfortunately, the assumptions of many standard methods are not valid for complex environmental data sets, with a realistic statistical model requiring appropriate incorporation of scientific context. We examine two case studies in which the application of routine extreme value methods result in inappropriate models and inaccurate predictions. In the first scenario, incorporating random effects reflects shifts in unobserved climatic drivers that led to record-breaking US temperatures in 2021, permitting greater accuracy in return period prediction. In scenario two, a Gaussian mixture model fit to ice surface temperatures in Greenland improves fit and predictive abilities, especially in the poorly-defined upper tail around 0∘C.