Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM output from their native grids and at differing spatial resolutions to a common grid in order to be comparable to observed data and (2) bias correcting RCM data, via quantile mapping, for example, for future modeling and analysis. The uncertainty associated with (1) is not always considered for downstream operations in (2). This work examines this uncertainty, which is not often made available to the user of a regridded data product. This analysis is applied to RCM solar radiation data from the NA-CORDEX data archive and observed data from the National Solar Radiation Database housed at the National Renewable Energy Lab. A case study of the mentioned methods over California is presented.

翻译：统计降尺度的初始步骤需要能够比较来自区域气候模型（RCM）的观测数据。该预测要求：(1) 将RCM输出从其原始网格及不同空间分辨率重新插值到公共网格，以便与观测数据进行比较；(2) 通过分位数映射等方法对RCM数据进行偏差校正，用于未来建模与分析。与步骤(1)相关的不确定性在步骤(2)的下游操作中并未始终被考虑。本研究探讨了这种通常不向网格重插数据产品用户公开的不确定性。分析基于NA-CORDEX数据档案中的RCM太阳辐射数据，以及美国国家可再生能源实验室维护的国家太阳辐射数据库中的观测数据。研究还展示了上述方法在加利福尼亚州的案例应用。