DAWN-Cast: Dynamical Adaptive Wavelet Network for Precipitation Nowcasting

Precipitation nowcasting, the prediction of rain over the next minutes to hours, is critical for warning of flash floods and severe convective storms. Typically, weather radar data is used for this task and modeled as a video prediction task. Within an individual storm, rainfall patterns do not evolve uniformly: coherent, large-scale structures drift smoothly, whereas small-scale convective variations change suddenly and lose correlation within just a few frames. Existing nowcasting models capture multiscale spatial structure through Fourier decomposition, wavelets, convolutions, or generative modeling, yet they do not assign each precipitation scale its own temporal pathway. We propose DAWN-Cast, a $\text{D}$ynamical $\text{A}$daptive $\text{W}$avelet $\text{N}$etwork that lets every scale learn its own temporal evolution. Its Wavelet Guided Temporal Modeling (WGTM) block splits radar features into wavelet scale families and assigns each its own Frequency Adaptive Temporal (FAT) block, with no interaction between scales before reconstruction. Each FAT block pairs a nonlinear MLP with an adaptive, history-selective Gabor mapping. A refinement stage then reconciles the independently evolved scales by combining global spectral and local spatial interactions. Across four benchmark datasets, DAWN-Cast achieves the best result on 19 of 20 reported metrics, and ablations show that per-scale temporal operators outperform a shared one. DAWN-Cast also generalizes zero-shot to unseen radar domains and adapts to a new dataset by updating only 0.71% of its parameters.