<p>Multi-source precipitation merging (MSP) is widely used to improve the accuracy and spatial resolution of precipitation estimates for hydrological and water resources applications. However, most existing MSP methods assume fixed relationships between precipitation intensity and environmental variables, which limits their ability to represent nonlinear precipitation behavior and often results in degraded performance during heavy and extreme rainfall events. To overcome these limitations, this study proposes a three-step merging framework that integrates downscaling, precipitation event classification, and categorical merging. The framework first downscales original precipitation products to enhance spatial resolution, then classifies precipitation events according to occurrence and intensity, and finally models intensity-specific nonlinear relationships between precipitation and environmental variables using machine learning. Based on this approach, a high-resolution merged precipitation dataset, termed the Multi-Source Merging Precipitation dataset (MSMP), was developed at a 1&#xa0;km spatial resolution and daily temporal resolution for the period 1981–2020. The framework was evaluated over the Pearl River Basin in South China by comparison with original precipitation products and conventional MSP methods. Results indicate that the MSMP consistently outperforms existing products in both statistical and categorical metrics, particularly for heavy and extreme precipitation. These findings demonstrate that incorporating precipitation intensity classification significantly enhances multisource precipitation merging and provides more reliable precipitation inputs for hydrological modeling and water resources management.</p>