The difficulty of forecasting “normal” climate conditions is demonstrated in the context of bivariate normally distributed forecasts and observations. Deterministic and probabilistic skill scores for the normal category are less than for the outer category for all‐but‐perfect models. There are two important mathematical properties of the normal category in a three‐category climatologically equiprobable forecast system that affect the scores for this category. First, the normal category can achieve the highest probability less frequently than the outer categories, and far less frequently in contexts of weak to moderate skill. Second, there are upper limits to the probability the normal category can reach. These mathematical constraints suggest that summary measures of skill may underestimate the predictability and forecast‐skill of extreme events, and that subjective inputs to probabilistic forecasts may need to take greater account of limitations to the predictability of normal conditions.

中文翻译：

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“正常”的预测

在二元正态分布的预报和观测的背景下证明了预报“正常”气候条件的困难。对于所有完美模型，正常类别的确定性和概率技能得分低于外部类别。在三类气候学等概率预测系统中，正常类别有两个重要的数学属性会影响该类别的分数。首先，正常类别可以比外部类别更不频繁地获得最高概率，而在技能弱至中等的情况下则不那么频繁。其次，正常类别可以达到的概率有上限。这些数学上的约束表明，简单的技能测度可能会低估极端事件的可预测性和预测技能，