Increasing coastal inundation risk in a warming climate will require accurate and reliable seasonal forecasts of sea level anomalies at fine spatial scale. In this study, we explore statistical downscaling of monthly hindcasts from six current seasonal prediction systems to provide high resolution prediction of sea level anomalies along the North American coast, including at several tide gauge stations. This involves applying a seasonally-invariant downscaling operator, constructing by linearly regressing high-resolution (1/12º) ocean reanalysis data against its coarse-grained (1º) counterpart, to each hindcast ensemble member for the period 1982-2011. The resulting high resolution coastal hindcasts are significantly more skillful than the original hindcasts interpolated onto the high resolution grid. Most of this improvement occurs during summer and fall, without impacting the seasonality of skill noted in previous studies. Analysis of the downscaling operator reveals that it boosts skill by amplifying the most predictable pattern while damping the less predictable pattern.