AbstractPeatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE‐PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland‐specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO2 fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy‐covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single‐site calibration) or all sites simultaneously (multi‐site calibration). Single‐site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi‐site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait‐climate correlations). Site‐optimized parameters, such as Q10, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, Q10 decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single‐site optimization, we incorporated two key trait‐climate relationships for Q10 and Vcmax (maximum carboxylation rate) into a new version of the ORCHIDEE‐PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands.