• bottom-up approach;
  • climate change;
  • forcing and chilling requirements;
  • leaf unfolding;
  • regional unified model;
  • spatiotemporal prediction;
  • tree phenology


Using first leaf unfolding data of Salix matsudana, Populus simonii, Ulmus pumila, and Prunus armeniaca, and daily mean temperature data during the 1981–2005 period at 136 stations in northern China, we fitted unified forcing and chilling phenology models and selected optimum models for each species at each station. Then, we examined performances of each optimum local species-specific model in predicting leaf unfolding dates at all external stations within the corresponding climate region and selected 16 local species-specific models with maximum effective predictions as the regional unified models in different climate regions. Furthermore, we validated the regional unified models using leaf unfolding and daily mean temperature data beyond the time period of model fitting. Finally, we substituted gridded daily mean temperature data into the regional unified models, and reconstructed spatial patterns of leaf unfolding dates of the four tree species across northern China during 1960–2009. At local scales, the unified forcing model shows higher simulation efficiency at 83% of data sets, whereas the unified chilling model indicates higher simulation efficiency at 17% of data sets. Thus, winter temperature increase so far has not yet significantly influenced dormancy and consequent leaf development of deciduous trees in most parts of northern China. Spatial and temporal validation confirmed capability and reliability of regional unified species-specific models in predicting leaf unfolding dates in northern China. Reconstructed leaf unfolding dates of the four tree species show significant advancements by 1.4–1.6 days per decade during 1960–2009 across northern China, which are stronger for the earlier than the later leaf unfolding species. Our findings suggest that the principal characteristics of plant phenology and phenological responses to climate change at regional scales can be captured by phenological and climatic data sets at a few representative locations.