Spatiotemporal cross-attention hybrid network for weather forecasting in complex terrain

Weather forecasting in complex terrain, such as the karst landscapes of Guizhou, faces the dual challenge of capturing microscale topographic effects and macroscale atmospheric dynamics. Existing models often struggle to balance local spatiotemporal patterns with long-range dependencies. To address this, we present a novel hybrid architecture that integrates ConvLSTM and Transformer via a Spatiotemporal Cross-Attention (STCA) mechanism. Our approach utilizes an STL-VMD preprocessing pipeline to decompose non-stationary meteorological signals into stationary subsequences, effectively separating trend and seasonal components. The architecture synergizes ConvLSTM's local feature extraction with Transformer's global correlation modeling, while the STCA module dynamically bridges spatial and temporal information through multi-scale convolution and adaptive gating. Validated across 33 meteorological stations in Guizhou over a 51-year period (1961-2011), our model demonstrates superior performance. Compared to baseline LSTM models, temperature forecasting achieves an 18.99% reduction in RMSE ([Formula: see text]), and notably, precipitation forecasting achieves a 27.74% reduction in RMSE with a substantial 54.08% improvement in [Formula: see text]. The results confirm the model's robustness and generalization capability for operational forecasting in topographically complex regions.