Vegetation phenological shifts impact the terrestrial carbon and water cycle and affect the local climate system through biophysical and biochemical processes. Dynamic global vegetation models (DGVMs), serving as pivotal simulation tools for investigating climate impacts on terrestrial ecosystem processes, incorporate representations of vegetation phenological processes. Nevertheless, it is still a challenge to achieve an accurate simulation of vegetation phenology in the DGVMs. Here, we developed and implemented spring and autumn phenology algorithms into one of the DGVMs, LPJ-GUESS. The new phenology modules are driven by temperature and photoperiod and are parameterized for deciduous trees and shrubs by using remotely sensed phenological observations and the reanalysis data from ERA5. The results show that the LPJ-GUESS with the new phenology modules substantially improved the accuracy in capturing the start and end dates of growing seasons. For the start of the growing season, the simulated RMSE for deciduous trees and shrubs decreased by 8.04 and 17.34 d, respectively. For the autumn phenology, the simulated RMSE for deciduous trees and shrubs decreased by 22.61 and 17.60 d, respectively. Interestingly, we have also found that differences in the simulated start and end of the growing season also alter the simulated ecological niches and competitive relationships among different plant functional types (PFTs) and subsequentially influence the terrestrial carbon and water cycles. Hence, our study highlights the importance of accurate phenology estimation to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.