Abstract A promising means for increasing skill of seasonal predictions of Arctic sea ice is improving sea ice thickness (SIT) initial conditions; however, sparse SIT observations limit this potential. Using the Canadian Climate Model, version 3 (CanCM3), three statistical models designed to estimate SIT fields for initialization in a real-time forecasting system are applied to initialize sea ice hindcasts over 1981–2012. Hindcast skill is assessed relative to two benchmark SIT initialization methods (SIT-IMs): a climatological initialization currently used operationally and SIT values from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). Based on several measures of skill, sea ice predictions are generally improved relative to a climatological initialization. The accuracy with which the initialization fields represent both the thinning of the ice pack over time and interannual variability impacts predictive skill for pan-Arctic sea ice area (SIA) and regional sea ice concentration (SIC), with the most robust improvements obtained with SIT-IMs that best represent both processes. Similar skill to that achieved by initializing with PIOMAS, including skillful predictions of detrended September SIA from May, is obtained by initializing with two of the statistical models. Regional skill for September SIC is also enhanced using improved SIT-IMs, with an increase in the spatial coverage of statistically significant skill from 10% to 60%–70% of the appreciably varying ice pack. Reduced skill is seen, however, in the Nordic seas using the improved SIT-IMs, resulting from an inherent cold sea surface temperature bias in CanCM3 that is amplified by a thicker initial ice cover.