<p>We examine several springtime plant phenology indices calculated from a set of statistically downscaled daily minimum and maximum temperature projections. Multiple statistical downscaling methods are used to refine daily temperature projections from multiple global climate models (GCMs) run with multiple radiative forcing scenarios. Focusing on the northeastern United States, the statistically downscaled temperature projections are input to a commonly used Extended Spring Indices (SI-x) model, yielding yearly estimates of phenological indices such as First Leaf Date (an early spring indicator), First Bloom Date (a late spring indicator), and the occurrence of Late False Springs (a year in which a hard freeze occurs after first bloom, when plants are vulnerable to damage from freezing conditions). The matrix of results allows one to analyze how projected spring phenological index differences arising from the choice of statistical downscaling method (i.e., the statistical downscaling uncertainty) compare with the magnitudes of variations across the different GCMs (climate model uncertainty) and radiative forcing pathways (scenario uncertainty). As expected, the onset of spring in the late 21<sup>st</sup> century projections, as measured by First Leaf and First Bloom Dates, typically shifts multiple weeks earlier in the year compared with the historical period. Those two start-of-spring indices can be thought of as being largely, but not entirely, dependent on an accumulation of heat since 1 January. In contrast, a Late False Spring occurs in large part due to a short-term weather event - namely if any single day after the First Bloom Date has a minimum temperature below -2.2C. Accordingly, spring phenological indices calculated from statistically downscaled climate projections can be influenced by how well the GCM&#8217;s historical simulation represents temperature variations on different time scales (diurnal temperature range, synoptic time-scale temperature variability, inter-annual temperature variations) as well as how a particular statistical refinement method (e.g., a delta change factor method, a quantile-based bias correction method, or a constructed analog method) combines information gleaned from both the GCM time series and the observation-based training data to generate the statistically refined daily maximum and minimum temperature time series. Though this study is limited in scope (northeastern United States region, a finite set of statistical downscaling methods and GCMs), we believe the general findings likely are illustrative and applicable to a wider range of mid-latitude locations where plant responses in spring are mostly temperature and day length driven.</p>
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