Trustworthy Precipitation Prediction via Communication-Efficient Lightweight Trend Perception Network

Responsible and Trustworthy AI is essential for accurate precipitation prediction, which impacts agriculture, transportation, and disaster prevention, where errors can lead to severe economic losses and casualties. In the 6G era, AI models must be reliable in addressing small sample biases and extreme weather data scarcity, while remaining lightweight for efficient real-time deployment and ultra-fast communication. Most deep learning-based methods struggle to capture the temporal evolution of spatial features effectively. The limitations include insufficient awareness of temporal trends and a lack of precise control over the spatial representation of temporal features. To address these issues, we propose a Lightweight Trend Perception Network (LTPNet) for precipitation prediction. It consists of an encoder, a decoder, and two stacked layers of Trend Perception (TP) units. Specifically, in the TP unit, the spatial matching module is proposed to extract the spatio-temporal dependency information of the history in the current situation. At the same time, a time channel fusion module is proposed to capture the spatial variation trend of precipitation. Experimental evaluation on the WeatherBench dataset demonstrates that LTPNet outperforms other state-of-the-art methods objectively and subjectively.