In this study, we use a data assimilation framework based on the Adaptive Markov Chain Monte Carlo (MCMC) algorithm to constrain process parameters in LPJ-GUESS using CH<sub>4</sub> eddy covariance flux observations from 14 different natural boreal and temperate wetlands. The objective is to derive a single set of calibrated parameter values. These parameters are then used in the model to validate its CH<sub>4</sub> flux output against 5 different types of natural wetlands situated in different locations, assessing their generality for simulating CH<sub>4</sub> fluxes from different boreal and temperate wetlands. The results show that the MCMC framework has substantially reduced the cost function (measuring the misfit between simulated and observed CH<sub>4</sub> fluxes) and facilitated detailed characterisation of the posterior distribution. A reduction of around 95 % in the cost function and approximately 50 % in RMSE were observed. The validation experiment results indicate that four out of 5 sites successfully reduced RMSE, demonstrating the effectiveness of the framework for estimating CH<sub>4</sub> emissions from wetlands not included in the study.