The aim of this work was to reveal the differences in the macro- and microphysical characteristics and precipitation mechanisms of tropical precipitation systems (TPSs) in different regions. Based on the GPM satellite observation from 2014 to 2022, global TPSs were identified, and eight high-frequency areas were defined. Subsequently, their horizontal and vertical development, precipitation characteristics, and microphysical vertical structure were systematically analyzed. The results show that the horizontal development scale of TPSs is mostly between 10⁴ and 10⁵ km², with vertical development exceeding 10 km. The convective area fraction (CAF) ranges from 20% to 60%, and TPSs have a higher CAF and lower vertical development over the ocean than over land. Continental TPSs exhibit significantly stronger vertical development and more intense precipitation in convective cores than oceanic TPSs. The stronger vertical development over land is mainly attributed to stronger updrafts associated with topographic lifting, which further enhances ice-phase microphysical processes and increases ice particle size. Meanwhile, the intensified updrafts also lead to higher collision–coalescence efficiency in the liquid layer, and temperature perturbations over land further enhance turbulent collision efficiency. Together, these processes result in stronger precipitation intensity in the convective cores of continental TPSs. Stratiform regions are characterized by weak precipitation dominated by raindrop breakup with small regional differences. These findings clarify the key land–ocean disparities in TPSs and provide critical observational evidence for optimizing cloud microphysical parameterization schemes in numerical models.

Approaching precipitation forecast refers to the prediction of precipitation within a short time scale, which is usually regarded as a spatiotemporal sequence prediction problem based on radar echo maps. However, due to its reliance on single-image prediction, it lacks good capture of sudden severe convective events and physical constraints, which may lead to prediction ambiguities and issues such as false alarms and missed alarms. Therefore, this study dynamically combines meteorological elements from surface observations with upper-air reanalysis data to establish complex nonlinear relationships among meteorological variables based on multisource data. We design a Residual Spatiotemporal Convolutional Network (ResSTConvNet) specifically for this purpose. In this model, data fusion is achieved through the channel attention mechanism, which assigns weights to different channels. Feature extraction is conducted through simultaneous three-dimensional and two-dimensional convolution operations using a pure convolutional structure, allowing the learning of spatiotemporal feature information. Finally, feature fitting is accomplished through residual connections, enhancing the model’s predictive capability. Furthermore, we evaluate the performance of our model in 0–3 h forecasting. The results show that compared with baseline methods, this network exhibits significantly better performance in predicting heavy rainfall. Moreover, as the forecast lead time increases, the spatial features of the forecast results from our network are richer than those of other baseline models, leading to more accurate predictions of precipitation intensity and coverage area.