SDRNet: Coupled Rainfall-State Evolution for Multi-Step Tropical Cyclone Precipitation Forecasting

Tropical cyclone rainfall fields undergo spatial deformation and local intensity changes beyond the motion of the storm center. Multi-step forecasting must therefore respond to nonuniform evolution while retaining spatial skill at later lead times. Direct regression of rainfall fields leaves the evolution relation implicit in the output mapping, whereas explicit net-residual updates do not separately coordinate the weights assigned to existing rainfall and additions within each step. We propose the Storm-relative Deformation–Survival–Renewal Network (SDRNet), which treats the current rainfall field as an explicit state and constructs a coupled survival–renewal recurrence derived from closed-form integration along characteristics. The model redistributes existing rain areas through backward warping, uses shared decay to coordinate exponential retention and nonnegative effective renewal, and feeds predicted rainfall fields back to refresh structural conditions and the hidden state for subsequent evolution. In four-step forecasting over 24 h on the Corrected and Raw rainfall products of LagTCPMoS, using given retrospective best tracks, SDRNet improves the aggregate fractions skill score (FSS) by approximately 6% over the best external comparison method on both products and achieves the highest critical success index at 20 mm/6 h (CSI20). At +24 h, it retains 93.5% and 93.7% of its first-step FSS, respectively. Comparisons using the same backbone and analyses of rainfall evolution further support the roles of explicit state updates and dynamic feedback in retaining spatial skill across forecast steps.