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In extreme values, the block maxima procedure can provide a few observations to perform the inference using the generalized extreme values (GEV) distribution. In this case, an interesting alternative is considered to use the  r -largest order statistics (). This study aims in obtaining the posterior distribution for thebased on the Bayesian estimation using the Markov chain Monte Carlo method and the Metropolis–Hastings algorithm procedure. The novelty of this study is the introduction of a dynamic linear seasonal model to capture the parameters of thedistribution over time. Also, we propose an adapted dynamic criterion for choosing the optimal value of  r  for each time point. The proposed model exhibits good performance in the simulations. Moreover, the application of the proposed model is performed on the dataset of daily maxima temperature () in Teresina, Piaui, Brazil.

Dynamic linear seasonal models applied to extreme temperature data: a Bayesian approach using the r -larger order statistics distribution