In　recent　years,　Transformer-based　change　detection　(CD)　in　remote　sensing　images　has　achieved　significant　advances,　making　it　an　emerging　hot　research　topic.　However,　the　current　CD　methods　suffer　from　some　problems,　such　as　incomplete　detection　of　change　regions　and　missed　detection　of　small　change　regions.　In　this　article,　a　global　context-aware　Transformer　is　proposed　for　CD　tasks,　named　GCFormer,　to　address　above　issues　by　efficiently　enhancing　the　global　context　information.　It　is　fulfilled　from　two　aspects　based　on　the　hybrid　convolutional　neural　network　(CNN)　+　Transformer　framework.　First,　a　multireceptive-field　Conv-Attention　(MRFCA)　mechanism　is　designed,　which　combines　dilated　convolutions　with　multiple　rates　(DCMRs)　and　Conv-Attention,　fully　leveraging　the　advantages　of　convolution　operation　and　self-attention　mechanism.　It　is　embedded　at　the　highest　layer　of　CNN　to　extract　multireceptive-field　global　context　information.　Second,　a　context-aware　relative　position　encoding　(CRPE)　mode　is　proposed　to　replace　the　absolute　position　encoding　(APE)　mode　of　Transformer.　As　a　result,　it　can　capture　long-range　dependency　more　efficiently　and　further　enhance　the　global　context　information　extraction　and　representation　ability　of　the　network.　Experimental　results　on　three　public　benchmark　datasets　of　LEVIR-CD,　WHU-CD,　and　DSIFN-CD　show　that,　the　proposed　GCFormer　achieves　superior　detection　performance　with　lower　model　complexity　than　the　state-of-the-art　(SOTA)　Transformer-based　CD　methods.　The　source　code　is　available　at　https://github.com/yuwanting828/yuwanting828.github.io.