  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.
