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Representing contextual features at multiple scales is important for RGB-D SOD. Recently, due to advances in backbone convolutional neural networks (CNNs) revealing stronger multi-scale representation ability, many methods achieved comprising performance. However, most of them represent multi-scale features in a layer-wise manner, which ignores the fine-grained global contextual cues in a single layer. In this paper, we propose a novel global contextual exploration network (GCENet) to explore the performance gain of multi-scale contextual features in a fine-grained manner. Concretely, a cross-modal contextual feature module (CCFM) is proposed to represent the multi-scale contextual features at a single fine-grained level, which can enlarge the range of receptive fields for each network layer. Furthermore, we design a multi-scale feature decoder (MFD) that integrates fused features from CCFM in a top-down way. Extensive experiments on five benchmark datasets demonstrate that the proposed GCENet outperforms the other state-of-the-art (SOTA) RGB-D SOD methods. • A global contextual exploration network (GCENet) is proposed to exploit the role of multi-scale features at a single fine-grained level for RGB-D SOD, where a cross-modal contextual feature module (CCFM) is designed to extract and fuse cross-modal and multi-scale features at a single micro level. • To integrate the multi-scale from multiple blocks of the backbone, we design a multi-scale feature decoder (MFD) to fuse these multi-scale features in a top-down layer-wise strategy, where the top features are embedded into all the features of the lower layer by a fully transferred strategy. • Extend experiments on five benchmark datasets demonstrate that the proposed GCENet outperforms other 18 well-known RGB-D SOD methods and achieves state-of-the-art performance in terms of five widely used evaluation metrics. [ABSTRACT FROM AUTHOR]