Deep learning for precipitation forecasting remains constrained by complex meteorological factors affecting accuracy. To address this issue, this paper proposes TransMambaCNN, which is a spatiotemporal transformer network fusing state-space models and CNNs for short-term precipitation forecasting. The core of the model employs a Convolutional State-Space Module (C-SSM), which efficiently extracts spatiotemporal features from multi-source meteorological variables by replacing the self-attention mechanism in the Vision Transformer (ViT) with an Attentive State-Space Module (ASSM) and augmenting its feature extraction capacity with integrated depthwise convolution. Its dual-branch architecture consists of a global branch, where C-SSM captures long-range dependencies and global spatiotemporal patterns, and a local branch, which leverages multi-scale convolutions based on SimVP’s Inception structure to extract fine-grained local features. The deep fusion of these dual branches significantly enhances spatiotemporal feature representation.Experiments demonstrate that in southeastern China and adjacent marine areas (period of high precipitation: April–September), TransMambaCNN achieves a 13.38% and 47.67% improvement in Threat Score (TS) over PredRNN at thresholds of ≥25 mm and ≥50 mm, respectively. In the Qinghai Sanjiangyuan region of western China (a precipitation-scarce area), TransMambaCNN’s TS score surpasses SimVP by 11.86 times at the ≥25 mm threshold.