  Precipitation nowcasting is an important spatio-temporal prediction task to
predict the radar echoes sequences based on current observations, which can
serve both meteorological science and smart city applications. Due to the
chaotic evolution nature of the precipitation systems, it is a very challenging
problem. Previous studies address the problem either from the perspectives of
deterministic modeling or probabilistic modeling. However, their predictions
suffer from the blurry, high-value echoes fading away and position inaccurate
issues. The root reason of these issues is that the chaotic evolutionary
precipitation systems are not appropriately modeled. Inspired by the nature of
the systems, we propose to decompose and model them from the perspective of
global deterministic motion and local stochastic variations with residual
mechanism. A unified and flexible framework that can equip any type of
spatio-temporal models is proposed based on residual diffusion, which
effectively tackles the shortcomings of previous methods. Extensive
experimental results on four publicly available radar datasets demonstrate the
effectiveness and superiority of the proposed framework, compared to
state-of-the-art techniques. Our code is publicly available at
https://github.com/DeminYu98/DiffCast.
