A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

越来越多的研究旨在描述人为排放对极端天气和气候事件可能性的贡献。这些分析可能对模拟分布的尾部形状很敏感。如果发现尾巴太短或太长，则可能的天气噪声会分别或多或少地清晰地显示出人为信号。在这里，我们比较了通常用于事件归因的三种大气全球气候模型与六种再分析产品的分布所产生的每日地表降水和近地表极端温度的机会。比较极端概率的可能性，以获取网格单元和区域大小的空间域上的平均面积。结果表明，偏向于偏向非洲和澳大利亚许多地区的高温和低温事件的过高归因估计，偏向于偏向北美洲和亚洲地区的过冷归因估计。对于降雨，结果对地理位置更加敏感。尽管这三个模型在许多地区都显示出相似的结果，但它们在其他地区确实存在分歧。同样，结果突出了重新分析产品之间的差异。这强调了在事件归因研究中使用多种重新分析和/或观察产品以及多种模型的重要性。他们确实不同意别人。同样，结果突出了重新分析产品之间的差异。这强调了在事件归因研究中使用多种重新分析和/或观察产品以及多种模型的重要性。他们确实不同意别人。同样，结果突出了重新分析产品之间的差异。这强调了在事件归因研究中使用多种重新分析和/或观察产品以及多种模型的重要性。