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Rainfall nowcasting of deep convection in the tropics is extremely challenging, particularly in highly urbanized coastal regions such as Singapore, where high spatial resolution is required. Conventional optical flow-based nowcasting methods typically struggle with capturing the initiation, duration, and spatiotemporal evolution of deep convection and rainfall. When it comes to extreme rainfall, these existing methods cannot deliver skillful nowcasts due to rapid changes in localized features of individual deep convection events. Recent advances in AI-based data-driven models, particularly deep generative models utilizing high-resolution radar imagery, have improved nowcasting accuracy at longer lead times. However, they often serve as black boxes, neglecting the underlying physics, potentially missing unseen extremes, and underestimating their rainfall intensity. To better tackle convection onset prediction, we adopt a novel importance sampling strategy that targets convective initiation by identifying convective cells based on a 35 dBZ threshold and fitting a linear growth trend across frames. Samples with steeper growth and fewer initial convective cells are prioritized to emphasize early-stage development. To enhance physical realism in deep tropics, we further propose a physics-informed deep generative model that incorporates diurnal and seasonal cycles to reflect tropical weather variability. Moreover, the model includes three-dimensional physical information such as Doppler wind and multi-altitude reflectivity. With the incorporation of additional physical information, the proposed generative framework consistently outperforms baseline models, particularly at early forecast lead times. Relative to the original DGMR driven solely by precipitation inputs, the physics-informed model achieves substantially higher skill across multiple rainfall thresholds. Over a 90-min forecast horizon, the average probabilities of detection (POD) reach 0.70, 0.47, and 0.21 at 1.0, 4.0, and 16.0 mm h⁻¹, corresponding to relative improvements of 27%, 25%, and 25%, respectively, with associated critical success indices (CSI) of 0.47, 0.30, and 0.15. In addition, spatial correlation is enhanced across pooling scales of 0.5, 2.0, and 8.0 km, yielding average Pearson correlation coefficients (PCC) of 0.27, 0.32, and 0.46, representing relative gains of 15–16% compared with the baseline. Attribution analysis further indicates that multi-altitude reflectivity contributes most strongly to nowcasting skill, followed by composite reflectivity, while the influence of time-regime information increases with forecast lead time and the contribution of three-dimensional wind fields remains comparatively modest. Our novel physics-informed deep generative model provides valuable insight into convective precipitation processes, supports more reliable nowcasting, and helps guide future data collection in tropical regions.