Counterfactual　reasoning　has　recently　achieved　impressive　performance　in　the　explainability　of　recommendation.　However,　existing　counterfactual　explainable　methods　ignore　the　realism　of　explanations　and　consider　only　the　sparsity　and　proximity　of　explanations.　Moreover,　the　huge　counterfactuals　space　causes　a　time-consuming　search　process.　In　this　study,　we　propose　Prototype-Guided　Counterfactual　Explanations　(PGCE),　a　novel　counterfactual　explainable　recommendation　framework　to　overcome　the　above　issues.　At　its　core,　PGCE　leverages　a　variational　auto-encoder　generative　model　to　constrain　the　modification　of　features　to　generate　counterfactual　instances　that　are　consistent　with　the　distribution　of　real　data.　Meanwhile,　we　constructed　a　contrastive　prototype　for　each　user　in　a　low-dimensional　latent　space,　which　can　guide　the　search　direction　towards　the　optimal　candidate　instance　space,　thus,　speed　up　the　search　process.　For　evaluation,　we　compared　our　method　with　several　state-of-the-art　model-intrinsic　methods　on　three　real-world　datasets,　in　addition　to　the　latest　counterfactual　reasoning-based　method.　Extensive　experiments　show　that　our　model　is　not　only　able　to　efficiently　generate　realistic　counterfactual　explanations　but　also　achieve　state-of-the-art　performance　on　other　popular　explainability　evaluation　metrics.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.