The use of Counterfactual Explanations (CEs) has made artificial intelligence models more accessible and understandable to humans. To generate CEs, Counterfactual Examples (CFs) are employed, as they allow us to identify the changes required to obtain a different prediction. The development of these techniques has been particularly prominent in models addressing classification problems, as evidenced by the wide range of frameworks and approaches available. However, such development has not been thoroughly explored for models that deal with regression problems. Therefore, we present Fuzzy Counterfactuals Examples (FUCO), a framework capable of generating CFs in regression problems while considering the needs of the model’s stakeholders. Our framework defines CFs by dividing the response space of the model’s predictions while considering the constraints and limitations of the stakeholders. Thanks to this partitioning, the use of FUCO enables the identification of Fuzzy Counterfactuals that lie within the response space and can be used as CFs owing to their proximity to the observation under analysis.