RainODE: Continuous-Time Precipitation Forecasting with Latent Neural ODEs

In precipitation forecasting, not only accuracy but also tem-poral resolution is critical. However, increasing temporal resolution isconstrained by observational limitations and the computational cost ofdense discrete modeling. To overcome this limitation, we reformulate pre-cipitation forecasting as a continuous-time dynamical system and pro-pose RainODE, a framework that models precipitation evolution in la-tent space using a Neural ODE. This formulation enables derivative-consistent temporal dynamics and captures the dominant large-scaleadvective motion of precipitation systems. Nevertheless, a purely de-terministic ODE struggles to represent non-advective intensity changessuch as localized growth, decay, and sub-grid variability, often lead-ing to over-smoothed predictions. To address this issue, we introducea stochastic source modeling module based on a Brownian Bridge for-mulation, which refines residual intensity variations and restores fine-grained structures while preserving advective consistency. By combiningdeterministic continuous dynamics with stochastic refinement, RainODEenables arbitrary-time inference while maintaining sharp predictions. Ex-periments on SEVIR and the newly introduced Radar-based Precipita-tion Integrated Dataset (RAPID) demonstrate consistent improvementsacross multiple temporal intervals and precipitation regimes. The codeis available at https://github.com/SeongYE/RainODE.