Evaluating machine learning approaches to improve observational daily precipitation datasets

Accurate precipitation predictions are vital for water resource management and risk mitigation. Interpolated precipitation estimates derived from in situ observations are frequently used to evaluate climate models and analyse trends. However, these inadequately represent its spatio-temporal characteristics and significantly smooth out extremes, inhibiting effective evaluation of dynamical models and analysis of trends. Machine learning methods may be suited to addressing these limitations due to their ability to identify patterns in large datasets and use of GPU acceleration. Therefore, we compare three ML-based approaches for improving observational daily precipitation datasets: Gaussian Processes, Bayesian Neural Fields, and Neural Processes. Their performance is evaluated using traditional and distributional metrics, including on out-of-sample prediction, enabling an objective assessment of generalisation skill and representation of extremes. Results are further compared against existing precipitation products to identify the relative strengths and limitations of each method.