# Trend-Aware Attention

Near-surface air temperature forecasts 24 hours ahead must capture the daily cycle, synoptic evolution and hourly fluctuations at once, while common decomposition transformers defer or hide cross-variable interactions. A cross-channel temporal correlation transformer exchanges information among meteorological channels at the embedding stage via 1×1 convolutions, then fuses temporal, trend and raw branches with gated attention and multi-scale trend attention. On six Chinese city datasets it lowers MSE by 6.2% and MAE by 7.5% on average against mainstream models, with MSE reduction above 9% in Chongqing's extreme heat.

Near-surface air temperature forecasts 24 hours ahead must capture the daily cycle, synoptic evolution and hourly fluctuations at once, while common decomposition transformers defer or hide cross-variable interactions. A cross-channel temporal correlation transformer exchanges information among meteorological channels at the embedding stage via 1×1 convolutions, then fuses temporal, trend and raw branches with gated attention and multi-scale trend attention. On six Chinese city datasets it lowers MSE by 6.2% and MAE by 7.5% on average against mainstream models, with MSE reduction above 9% in Chongqing's extreme heat.