In spring many plants break dormancy and begin foliage production. The appearance of leaves (the “green-up” period) triggers a rapid increase in transpiration at the surface as well as changes in albedo. Subsequently, these processes alter the thermodynamic properties of the surface layer. Normally, seasonal variations in tropospheric thickness, and year-to-year variability in the green-up date, mask the impact of these effects on surface maximum temperatures. In this study we used first leaf phenological data (from the clone lilac Syringa chinensis in the central and eastern United States) as the indicator of transpiration onset, in order to reveal the effects of this change from a dormant vegetative surface, perhaps comparable to the worst summertime droughts, to an active foliage-producing and transpiring vegetative surface. Simple plots using average hypsometric layer-mean temperature (derived from geopotential thickness) and average surface daily maximum temperature were examined for variations in the thickness-maximum temperature relationship relative to first leaf date. We then employed a multiple regression model to test the significance and estimate the magnitude of changes in this relationship. The model confirmed that there are statistically and practically significant relationships between the timing of the green-up period and surface daily maximum temperature. For the same thickness value, one station type (“A”; generally in agricultural inland areas) showed at least a 3.5°C reduction in surface daily maximum temperatures over any two-week period subsequent to first leaf compared to a two-week period prior to first leaf. Station locations generally near major water bodies (type “B”) showed a smaller (1.5°C) reduction. From these results we infer that even without feed backs between the surface vegetation and the atmospheric circulation, the surface daily maximum temperature may be significantly altered. During extreme drought, which produces widespread plant wilt, similar effects may be expected. In addition, the results indicate that the daily weather forecasts from specification and model output statistics equations may be significantly improved during spring by inclusion of a green-up variable.