Towards a Process-Informed Framework for Assessing the Credibility of Statistical and Dynamical Downscaling Methods

This study presents a process-informed framework for assessing the differential credibility of diverse downscaling methodologies, including both statistical (simple and complex) and dynamical approaches. The methods evaluated include a convolutional neural network (CNN), the Locally Constructed Analog Method (LOCA), the Statistical DownScaling Model (SDSM), quantile delta mapping (QDM), simple interpolation with bias correction, and two regional climate models. As proof of concept, we apply the framework to evaluate the physical consistency of processes associated with wet-day occurrence at a site in the southern USA Great Plains. Additionally, we introduce a relative credibility metric that quantifies cross-method performance and outlines how this framework can be extended to other variables, regions, and downscaling applications. Results show that all downscaling methods perform credibly when the parent global climate model (GCM) performs credibly. However, complex statistical methods (CNN, LOCA, SDSM) tend to exacerbate GCM errors, while simpler methods (QDM, interpolation + bias correction) generally preserve GCM credibility. Dynamical downscaling, by contrast, can mitigate inherited biases and improve overall process-level credibility. These findings underscore the importance of process-based evaluation in downscaling assessments and reveal how downscaling model complexity interacts with GCM quality.