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The predictability of spring onset is assessed using an index of its interannual variability (the "extended spring index" or SI-x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI-x were treated with a daily joint bias correction approach, and the SI-x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approachnonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35 degrees N to 45 degrees N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI-x fall into two categories: 10-40 and 40-60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10-30 days. The postprocessing work using joint bias correction improves the predictive skill for SI-x relative to the untreated input data set. Using nonhomogeneous Gaussian regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales.