Abstract We devise a variational Bayes algorithm for fast approximate inference in Bayesian Generalized Extreme Value additive model analysis. Such models are useful for flexibly assessing the impact of continuous predictor variables on sample extremes. The new methodology allows large Bayesian models to be fitted and assessed without the significant computing costs of Monte Carlo methods. © 2010 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Alfred Stein.