• Proposes the Multimodal Bivariate Extreme Value (MMBEV) distribution. • Extends bivariate extreme value theory to capture multimodal dependencies. • Applies MMBEV to model extreme climate data with complex dependencies. • Demonstrates MMBEV’s flexibility through simulations and real-world analysis. • Establishes links between MMBEV and bivariate Weibull distributions. Modeling multivariate extreme values in complex systems increasingly demands flexible distributions capable of capturing multimodal behavior. Classical bivariate extreme value (BEV) distributions, though well established, are inherently unimodal and may inadequately represent data with heterogeneity or regime-switching—features commonly observed in environmental and financial applications. This paper introduces the Multimodal Bivariate Extreme Value (MBEV) distribution, a new class that extends the traditional BEV framework to accommodate multimodal structures through additional shape parameters. Unlike previous generalizations restricted to specific cases such as the bivariate Gumbel model, the MBEV encompasses the entire BEV family. We explore theoretical properties of the MBEV model, including its stochastic representation and its connection to the bivariate Weibull distribution. Through Monte Carlo simulations, we assess the performance of maximum likelihood estimators and demonstrate their robustness. An application to climate data from Brasília, Brazil, illustrates the practical value of the MBEV model in capturing complex dependencies among extreme variables such as wind gust speed, relative humidity, and dew point temperature. Model selection criteria (AIC and BIC) confirm the superiority of the MBEV model over classical BEV distributions. Overall, the MBEV model offers a flexible and interpretable framework for modeling bivariate extremes, with potential applications in climatology, survival analysis, reliability, and finance. This work advances the frontier of extreme value modeling by addressing multimodal dependence in heterogeneous multivariate contexts.