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Climate change causes both temporal (e.g. advancing spring phenology) and geographic (e.g. range expansion poleward) species shifts, which affect the photoperiod experienced at critical developmental stages (‘experienced photoperiod’). As photoperiod is a common trigger of seasonal biological responses – affecting woody plant spring phenology in 87% of reviewed studies that manipulated photoperiod – shifts in experienced photoperiod may have important implications for future plant distributions and fitness. However, photoperiod has not been a focus of climate change forecasting to date, especially for early‐season (‘spring’) events, often assumed to be driven by temperature. Synthesizing published studies, we find that impacts on experienced photoperiod from temporal shifts could be orders of magnitude larger than from spatial shifts (1.6 h of change for expected temporal vs 1 min for latitudinal shifts). Incorporating these effects into forecasts is possible by leveraging existing experimental data; we show that results from growth chamber experiments on woody plants often have data relevant for climate change impacts, and suggest that shifts in experienced photoperiod may increasingly constrain responses to additional warming. Further, combining modeling approaches and empirical work on when, where and how much photoperiod affects phenology could rapidly advance our understanding and predictions of future spatio‐temporal shifts from climate change.

To understand and forecast biological responses to climate change, scientists frequently use field experiments that alter temperature and precipitation. Climate manipulations can manifest in complex ways, however, challenging interpretations of biological responses. We reviewed publications to compile a database of daily plot‐scale climate data from 15 active‐warming experiments. We find that the common practices of analysing treatments as mean or categorical changes (e.g. warmed vs. unwarmed) masks important variation in treatment effects over space and time. Our synthesis showed that measured mean warming, in plots with the same target warming within a study, differed by up to 1.6 C (63% of target), on average, across six studies with blocked designs. Variation was high across sites and designs: for example, plots differed by 1.1 C (47% of target) on average, for infrared studies with feedback control (n = 3) vs. by 2.2 C (80% of target) on average for infrared with constant wattage designs (n = 2). Warming treatments produce non‐temperature effects as well, such as soil drying. The combination of these direct and indirect effects is complex and can have important biological consequences. With a case study of plant phenology across five experiments in our database, we show how accounting for drier soils with warming tripled the estimated sensitivity of budburst to temperature. We provide recommendations for future analyses, experimental design, and data sharing to improve our mechanistic understanding from climate change experiments, and thus their utility to accurately forecast species’ responses.