Climate models are biased with respect to real world observations and usually need to be calibrated prior to impact studies. The suite of statistical methods that enable such calibrations is called bias correction (BC). However, current BC methods struggle to adjust for temporal biases, because they disregard the dependence between consecutive time-points. As a result, climate statistics with long-range temporal properties, such as heatwave duration and frequency, cannot be corrected accurately, making it more difficult to produce reliable impact studies on such climate statistics. In this paper, we offer a novel BC methodology to correct for temporal biases. This is made possible by i) re-thinking BC as a probability model rather than an algorithmic procedure, and ii) adapting state-of-the-art machine-learning (ML) probabilistic attention models to fit the BC task. With a case study of heatwave duration statistics in Abuja, Nigeria, and Tokyo, Japan, we show striking results compared to current climate model outputs and alternative BC methods.

翻译：气候模型相对于真实观测存在偏差，通常需要在影响研究前进行校准。能够实现此类校准的统计方法统称为偏差校正。然而，现有偏差校正方法难以处理时序偏差，因为它们忽略了连续时间点之间的依赖性。这导致具有长程时序特性的气候统计量（如热浪持续时间和频率）无法被准确校正，使得针对此类气候统计量的可靠影响研究更加困难。本文提出了一种新颖的偏差校正方法，用于纠正时序偏差。该方法通过以下两个步骤实现：i) 将偏差校正重新理解为概率模型而非算法流程；ii) 采用最先进的机器学习概率注意力模型来适配偏差校正任务。通过尼日利亚阿布贾和日本东京的热浪持续时间统计案例研究，我们展示了相较于当前气候模型输出和替代偏差校正方法的显著改进效果。