Precipitation is crucial for the future development of mankind. However, accurately predicting it remains a formidable challenge. Due to the low efficiency of traditional Numerical Weather Prediction (NWP), deep-learning based methods are highly preferred. However, most deep learning methods focus on predicting the spatio-temporal behavior of the single precipitation variable, often ignoring the interplay between various meteorological factors and precipitation. Furthermore, they tend to underestimate it. Therefore, this paper proposes a new neural network model called Spatio-temporal Perceiving Network Based Vision Transformer (ST-ViT), which integrates spatio-temporal and channel perception mechanisms to build the relationship between precipitation and meteorological elements. Additionally, an adaptive differential loss function is proposed to accurately capture precipitation intensity. We evaluated the ST-ViT on ERA5 from Southeast Asia for 6h prediction. The quantitative results demonstrate that our method achieved superior accuracy and lower errors compared to other deep learning methods. Specifically, it shows great potential to alleviate the situation of underestimated precipitation from the reconstructed predicted image.