Spring leaf phenology and its response to climate change have crucial effects on surface albedo, carbon balance, and the water cycle of terrestrial ecosystems. Based on long-term (period 1963–2014) in-situ observations of budburst date (BBD) and leaf unfolding date (LUD) of more than 300 deciduous woody species from 32 sites across the temperate zone in China, we conducted model-data comparison of spring leaf phenology and temperature sensitivities (S_T) for 10 existing terrestrial ecosystem models (TEMs). These TEMs integrated three kinds of phenology models, namely simple temperature model, accumulated temperature model and alternating model. Our results suggested that alternating models performed the best in reproducing the spatial patterns of spring leaf phenology, but tended underestimate the temporal variations in responding to temperature warming, showing low S_T. In contrast, the performances of accumulated temperature models were the best in modelling S_T, but there were notable biases in modelling the spatial patterns of spring leaf phenology. Simple temperature models failed to reproduce both spatial and temporal patterns. All models underestimated the inter-annual variabilities in spring leaf phenology at almost all sites. This may be partly attributable to the non-dynamic model parameters, inferring form the better performances of phenology models with localized parameters. Using temperature series (1960–2100) form Coupled Model Intercomparison Project Number 6 (CMIP6) scenarios, our results highlighted large uncertainties in predicting spring leaf phenology changes with the warming climate, and more work is required to deal with the deficiencies of phenology model parameters and algorithms.

budburst date, leaf unfolding date, phenology model, chilling accumulation, growing degree-day, temperature sensitivity