发表机构
EDF R&D（法国电力集团研发部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对气候降尺度评估中单一指标误导排名的问题，提出多指标基准，比较五种方法在ERA5数据上的表现，揭示空间保真度与细尺度变率的权衡，强调多指标评估的必要性。
AI 中文摘要
气候降尺度旨在从粗分辨率输入重建细尺度空间场。评估这些重建的质量具有挑战性：低逐点误差可能以细尺度变率为代价，而逼真的空间变率可能伴随不准确的局部结构实现。因此，评估指标可能改变哪种方法表现最佳。本工作提出了一个多指标基准，比较了五种空间降尺度方法在ERA5温度、风和降水场上的表现。五个标准评估互补属性：逐点误差、结构相似性、分布误差、谱误差和梯度误差。结果揭示了空间保真度与细尺度变率之间的系统性权衡。一些方法在逐点和对齐空间指标上表现最佳，但丢失高频内容，而其他方法以较不准确的局部结构定位为代价，保留了显著更多的谱变率。因此，方法排名随指标和变量而变化。这些结果表明，不存在单一最佳降尺度方法。因此，多指标评估对于评估气候场的哪些属性被保留至关重要。
英文摘要
Climate downscaling aims to reconstruct fine scale spatial fields from coarse resolution inputs. Evaluating the quality of these reconstructions is challenging: low pointwise error can come at the cost of fine scale variability, while realistic spatial variability can be achieved with inaccurate local structures. The evaluation metric can therefore change which method appears to perform best. This work presents a multi metric benchmark comparing five spatial downscaling methods on ERA5 temperature, wind, and precipitation fields. Five criteria assess complementary properties: pointwise error, structural similarity, distribution error, spectral error, and gradient error. The results reveal a systematic trade off between spatial fidelity and fine scale variability. Some methods perform best on pointwise and spatially aligned metrics, but lose high frequency content, while others preserve substantially more spectral variability at the cost of less accurately positioned local structures. Consequently, method rankings change across metrics and variables. These results show that there is no single best downscaling method. Multi metric evaluation is therefore essential for assessing which properties of a climate field are preserved.
