Summary: The timing of diel stem growth of mature forest trees is still largely unknown, as empirical data with high temporal resolution have not been available so far. Consequently, the effects of day–night conditions on tree growth remained uncertain. Here we present the first comprehensive field study of hourly‐resolved radial stem growth of seven temperate tree species, based on 57 million underlying data points over a period of up to 8 yr. We show that trees grow mainly at night, with a peak after midnight, when the vapour pressure deficit (VPD) is among the lowest. A high VPD strictly limits radial stem growth and allows little growth during daylight hours, except in the early morning. Surprisingly, trees also grow in moderately dry soil when the VPD is low. Species‐specific differences in diel growth dynamics show that species able to grow earlier during the night are associated with the highest number of hours with growth per year and the largest annual growth increment. We conclude that species with the ability to overcome daily water deficits faster have greater growth potential. Furthermore, we conclude that growth is more sensitive than carbon uptake to dry air, as growth stops before stomata are known to close.

Radial stem growth dynamics at seasonal resolution are essential to understand how forests respond to climate change. We studied daily radial growth of 160 individuals of seven temperate tree species at 47 sites across Switzerland over 8 years. Growth of all species peaked in the early part of the growth season and commenced shortly before the summer solstice, but with species‐specific seasonal patterns. Day length set a window of opportunity for radial growth. Within this window, the probability of daily growth was constrained particularly by air and soil moisture, resulting in intermittent growth to occur only on 29 to 77 days (30% to 80%) within the growth period. The number of days with growth largely determined annual growth, whereas the growth period length contributed less. We call for accounting for these non‐linear intra‐annual and species‐specific growth dynamics in tree and forest models to reduce uncertainties in predictions under climate change.