GraphIDW: Incorporating spatial autocorrelation in satellite–gauge precipitation merging using graph neural networks over a tropical region

Abstract. Ground-based rain gauges remain the benchmark for accurate precipitation measurement; however, their sparse spatial distribution limits the representation of rainfall heterogeneity. Satellite-based Precipitation Products (SPPs) provide consistent spatial coverage but are often affected by retrieval errors and regional biases, restricting their direct use in local-scale hydrological applications. To overcome these limitations, Precipitation Data Merging (PDM) techniques integrating gauge and satellite observations have gained prominence. This study introduces a novel Machine Learning (ML) framework, GraphIDW, which combines Graph Neural Networks (GNNs) with Inverse Distance Weighting (IDW) interpolation to explicitly incorporate spatial autocorrelation into the merging process, addressing a major limitation of traditional ML-based PDM approaches. The framework was evaluated across the Wet Zone of Sri Lanka from 2001 to 2015 using two state-of-the-art SPPs (IMERG and CHIRPS) together with ground observations. IMERG data (0.1°) were first downscaled to 0.05° using CHIRPS, after which the downscaled product was merged with gauge observations through GraphIDW. A total of 60 gauges (70 %) were used for training and 28 (30 %) for validation. Results show that GraphIDW outperforms conventional ML algorithms, including Random Forest, Artificial Neural Network, Support Vector Regression, and XGBoost. 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, compared with the original SPPs. The results demonstrate that explicitly accounting for spatial dependence through graph-based learning significantly improves precipitation estimation, particularly in regions characterized by strong spatial heterogeneity. By embedding spatial autocorrelation directly into the merging process, GraphIDW provides a robust and computationally efficient framework for generating high-resolution rainfall datasets that are better suited for hydrological analysis in complex climatic and topographic settings.