Downscaling is the process by which low-resolution climate change information can be transformed to high-resolution for use across localities (e.g., Los Angeles, California) in addition to the whole of regions (e.g., all of California). In the dynamical method, low-resolution information from Global Climate Models (GCMs) is fed to Regional Climate Models (RCMs), which use fundamental laws of nature to simulate the earth system across a restricted area of the planet in high-resolution. Oftentimes however, the granulated outcome can be plagued with biases that can be larger in magnitude than the native GCM being downscaled. This is problematic, especially when considering that state and national governments are building their latest climate change assessments upon newly created downscaled model outputs, which subsequently inform decision-making and infrastructure planning. Using temperature, precipitation, and snow outputs from our recently developed Western United States Dynamically Downscaled Dataset (WUS-D3), we highlight the dangers of high-resolution and examine the sensitivity of end-product fidelity to regional climate modeling choices. We air our ‘dirty laundry’ in an attempt to challenge climate modelers and data users to avoid overvaluing downscaled climate datasets just because of their newness and granularity.