Empathetic　response　generation　endeavors　to　empower　dialogue　systems　to　perceive　speakers＇　emotions　and　generate　empathetic　responses　accordingly.　Psychological　research　demonstrates　that　emotion,　as　an　essential　factor　in　empathy,　encompasses　trait　emotions,　which　are　static　and　context-independent,　and　state　emotions,　which　are　dynamic　and　context-dependent.　However,　previous　studies　treat　them　in　isolation,　leading　to　insufficient　emotional　perception　of　the　context,　and　subsequently,　less　effective　empathetic　expression.　To　address　this　problem,　we　propose　Combining　Trait　and　State　emotions　for　Empathetic　Response　Model　(CTSM).　Specifically,　to　sufficiently　perceive　emotions　in　dialogue,　we　first　construct　and　encode　trait　and　state　emotion　embeddings,　and　then　we　further　enhance　emotional　perception　capability　through　an　emotion　guidance　module　that　guides　emotion　representation.　In　addition,　we　propose　a　cross-contrastive　learning　decoder　to　enhance　the　model＇s　empathetic　expression　capability　by　aligning　trait　and　state　emotions　between　generated　responses　and　contexts.　Both　automatic　and　manual　evaluation　results　demonstrate　that　CTSM　outperforms　state-of-the-art　baselines　and　can　generate　more　empathetic　responses.　Our　code　is　available　at　https://github.com/wangyufeng-empty/CTSM.　©　2024　ELRA　Language　Resource　Association:　CC　BY-NC　4.0.