SLRainGrid-D05: High-Resolution Daily Precipitation Dataset for Sri Lanka Derived from Machine Learning and Satellite-Gauge Fusion

Long-term, accurate fine-scale precipitation estimates are essential for hydrological and climate-related analyses, particularly in regions characterized by strong spatial rainfall variability. This study introduces SLRainGrid-D05, the first high-resolution gridded daily precipitation dataset for Sri Lanka, developed at a spatial resolution of 0.05°×0.05° and covering the entire country, including the wet, intermediate, and dry climatic zones. Sri Lanka’s tropical climate exhibits pronounced spatial variability in annual rainfall, ranging from approximately 900 mm to 5,500 mm, which cannot be adequately captured by the sparsely distributed rain-gauge network alone. In addition, satellite-based precipitation products (SPPs) are known to exhibit considerable biases over the region.

To address these limitations, a spatially consistent gridded precipitation dataset was developed by merging ground-based observations with SPPs. An initial evaluation of two widely used SPPs, IMERG and CHIRPS, demonstrated that IMERG performs better at the daily time scale, while CHIRPS shows superior performance at monthly scale. Based on these findings, daily IMERG precipitation was downscaled from its native 0.1°×0.1° resolution to 0.05°×0.05° using CHIRPS rainfall as spatial reference information. The downscaled IMERG product was subsequently merged with rain-gauge observations using machine-learning-based approaches.

The study introduces a novel hybrid merging framework that integrates graph neural networks (GNN) with inverse distance weighting (IDW) to explicitly account for the spatial autocorrelation of rainfall. The proposed method was benchmarked against conventional machine-learning models, including random forest, extreme gradient boosting, support vector machines, and artificial neural networks. Results indicate that the hybrid GNN-IDW framework consistently outperforms these benchmark methods in both rainfall detection and magnitude estimation. Specifically, it achieved the highest probability of detection (0.97) and reduced root mean square error (RMSE) and mean absolute error (MAE) by 13-41% and 9-36%, respectively, relative to the original SPPs. The SLRainGrid-D05 dataset offers a reliable, high-resolution precipitation product and represents a valuable resource for hydrological modeling, climate analysis, and improved preparedness for hydrological extremes, supporting water resources assessment and management across Sri Lanka, with the proposed methodology also being transferable to other tropical regions.