  In extreme values theory, for a sufficiently large block size, the maxima
distribution is approximated by the generalized extreme value (GEV)
distribution. The GEV distribution is a family of continuous probability
distributions, which has wide applicability in several areas including
hydrology, engineering, science, ecology and finance. However, the GEV
distribution is not suitable to model extreme bimodal data. In this paper, we
propose an extension of the GEV distribution that incorporate an additional
parameter. The additional parameter introduces bimodality and to vary tail
weight, i.e., this proposed extension is more flexible than the GEV
distribution. Inference for the proposed distribution were performed under the
likelihood paradigm. A Monte Carlo experiment is conducted to evaluate the
performances of these estimators in finite samples with a discussion of the
results. Finally, the proposed distribution is applied to environmental data
sets, illustrating their capabilities in challenging cases in extreme value
theory.
