MIdAS bias adjustment of extremes using Theil-Sen extrapolation: Data and plotting scripts for GMD-publication

When bias adjusting climate model data using quantile mapping approaches, one needs to prescribe what to do at the tails of the distribution, where a larger range of data is likely encountered outside the calibration period. The end results is highly dependent on the method used. For the current study, submitted to the journal Geoscientific Model Development, under the name 'Robust handling of extremes in quantile mapping - "Murder your darlings"' by Berg et al. (2024), the MIdAS bias adjustment method is evaluated and extended with additional functionality to deal with issues with bias adjustment of extreme precipitation. This entry contains data for the annual precipitation sums, and annual maximum daily precipitation, for a domain over Scandinavia, including a reference data set and a large ensemble of Euro-CORDEX regional climate models before and after bias adjustment using a range of experiments, as well as scripts for analysing the data and producing the figures of the paper. Further, the entry contains the daily timeseries for the reference and climate models needed to repeat all the experiments in the paper, along with the published code for MIdAS. The readme.txt documents provides a guide to structure the data, perform the experiments and to reproduce the plots of the paper.在使用分位数映射（quantile mapping）方法对气候模式数据开展偏差校正时，研究者需明确分布尾部的处理方案——校准期外往往会出现更大范围的数据波动，且最终校正结果高度依赖所选用的校正方法。本研究已投稿至《地球科学模式发展（Geoscientific Model Development）》期刊，论文标题为《分位数映射中极端值的稳健处理——"Murder your darlings"》，作者为Berg等（2024），针对极端降水偏差校正中存在的问题，对MIdAS偏差校正方法（MIdAS bias adjustment method）进行了评估与功能扩展。本数据集包含斯堪的纳维亚区域的年降水总量与年最大日降水量数据，涵盖经多组试验进行偏差校正前后的参考数据集与大型Euro-CORDEX区域气候模式集合（Euro-CORDEX regional climate models），同时附带用于数据分析与论文插图制作的脚本。此外，本数据集还包含参考数据与气候模式的逐日时间序列，可用于复现论文中的全部试验，同时附带已公开的MIdAS代码。附带的readme.txt文件提供了数据整理、试验开展与论文插图复现的详细指南。