In order to adapt to a changing climate, policymakers need information about what to expect for the climate system. Typically local information about certain aspects of the climate system comes from regional climate models as well as from observational records. A regional climate model is a downscaled global circulation model, a mathematical model that describes, using partial differential equations, the temporal evolution of climate, oceans, atmosphere, ice, and land-use processes over a gridded spatial domain of interest. An important problem is understand how well regional models can reproduce observed climate variables.
Using two motivating analyses based on data from the Swedish Meteorological and Hydrological Institute, I will discuss different spatio-temporal modeling strategies that can be used to assess regional climate models using observational data. I will also outline the associated statistical and computational challenges in building hierarchical models using data sources with varying spatial and temporal support. My intention is to motivate a dialogue about the broader challenges underlying spatio-temporal climate model assessment.