This paper introduces a novel framework for a process-informed, differential assessment of credibility across various spatial and point-based downscaling methodologies, including both complex and simple statistical approaches, as well as dynamical downscaling. The methods assessed include a convolutional neural network (CNN), the locally constructed analog method (LOCA), the statistical downscaling model (SDSM), quantile delta mapping (QDM), simple spatial interpolation plus bias correction, the Regional Climate Model (RegCM) and the Weather Research and Forecasting Model (WRF). For proof of concept of our framework, our study focuses specifically on the physical consistency of wet days in a location in the southern US Great Plains. We find that all downscaling methods perform credibly when the parent global climate model (GCM) performs credibly. However, complex statistical methods like CNN, LOCA, and SDSM exacerbate inaccuracies when the GCM outputs are unreliable, performing worse as the GCM’s credibility decreases. On the other hand, simpler methods like QDM and bias-correction generally retain the GCM’s credibility. Notably, dynamical models can mitigate issues inherited from GCMs, enhancing the overall credibility of the data. These results highlight the need for careful evaluation of complex statistical downscaling techniques and suggest that further scrutiny is warranted. Finally, we summarize our process-informed analysis of credibility into a relative credibility metric, offering a quantifiable way to compare different downscaling approaches, and we provide guidance on the application and expansion of our framework for future research.
