A recurrent question in climate risk analysis is determining how climate change will affect heavy precipitation patterns. Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters. In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to different forcing effects. Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence. It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence. We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs. As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project. We combine the climatologically consistent subregions identified for all GCMs. This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence. Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.