In this paper, we introduce methods for evaluating climate model performance across spatial scales. These techniques are based on the “scale space” framework widely used in the image processing and computer vision communities. We discuss why the diffusion equation on the sphere provides a particularly attractive means of smoothing two-dimensional maps of global climate data. We establish that no structure is introduced into a map as an artifact of the smoothing procedure. This allows for the comparison of models and observations at multiple scales. As a test case for these methods, we compare the ability of high- and low-resolution versions of the Community Climate System Model (CCSM) to simulate the seasonal climatologies of surface air temperature (TAS), sea level pressure (PSL), and total precipitation rate (PR). For TAS, we find that the high-resolution model is better able to capture the boreal summer (JJA) climatological pattern at fine scales, although there is no such improvement in winter (DJF). We find the performances of the high- and low-resolution models to be similarly capable of capturing the summertime sea level pressure climatology at all scales. However, the high-resolution model PSL climatology is degraded for DJF, especially at larger scales. For both JJA and DJF precipitation climatologies, we find larger precipitation errors in the high-resolution model at the finest scales; however, performance at larger scales is improved.