There is increasing demand for information from climate and weather models across scientific domains. Models with effective spatial resolutions of 10s to 100s of kilometers are often ingested by applications that make predictions at much smaller scales. This requires translation of atmospheric information for applications to global change, hydrology, health, and more. Downscaling methods address this scale gap with dynamical, empirical-statistical, hybrid, and machine-learning methods. Such methods may introduce additional uncertainties that impact end users. Given myriad user needs and downscaling approaches, we invite presentations that evaluate our confidence in downscaling methods; enhance product utility with developer-user collaboration; develop downscaling approaches such as AI/ML-based; characterize techniques for evaluating downscaling skill and statistical reliability; and advance uptake of downscaled products in real-world decision-making. We especially welcome submissions that explore gaps in creating, assessing, and using regional-to-local downscaling products or explore differences in products and applications arising from differing methodologies.