Climate models are biased with respect to real-world observations. They usually need to be adjusted before being used in impact studies. The suite of statistical methods that enable such adjustments is called bias correction (BC). However, BC methods currently struggle to adjust temporal biases. Because they mostly 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. This makes it more difficult to produce reliable impact studies on such climate statistics. This paper offers a novel BC methodology to correct temporal biases. This is made possible by rethinking the philosophy behind BC. We will introduce BC as a time-indexed regression task with stochastic outputs. Rethinking BC enables us to adapt state-of-the-art machine learning (ML) attention models and thereby learn different types of biases, including temporal asynchronicities. With a case study of heatwave duration statistics in Abuja, Nigeria, and Tokyo, Japan, we show more accurate results than 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之前进行调整。能够实现这类调整的统计方法统称为偏差校正。然而，目前的偏差校正方法难以调整时间偏差，因为它们大多忽略了连续时间点之间的依赖性。因此，具有长期时间特性的气候统计量（如热浪持续时间和频率）无法得到准确校正，这使得对此类气候统计量进行可靠影响研究的难度加大。本文提出一种新颖的偏差校正方法来修正时间偏差，这通过重新思考偏差校正的核心理念得以实现。我们将偏差校正定义为一种带随机输出的时间索引回归任务。重新定义偏差校正使我们能够适配最先进的机器学习注意力模型，从而学习不同类型的偏差，包括时间异步性。通过对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，我们证明了该方法比当前气候模型输出及替代偏差校正方法具有更高的准确性。