Louise J. Slater , Georgios Blougouras, Liangkun Deng, Qimin Deng, Emma Ford, Anne Hoek van Dijke, Feini Huang, Shijie Jiang, Yinxue Liu, Simon Moulds , Andrew Schepen, Jiabo Yin, Boen Zhang

Machine learning (ML) is a powerful tool for hydrological modelling, prediction, dataset generation, model interpretation, and process discovery. As such, ML has become integral to the field of large-sample hydrology, where hundreds to thousands of river catchments are included within a single ML model to capture diverse hydrological behaviours and improve model generalisability. This manuscript outlines recent advances in ML for large-sample hydrology. We review new tools in explainable AI (XAI) and interpretability approaches, as well as challenges in these areas. Key research avenues for ML in large-sample hydrology include addressing inconsistencies in model interpretability, enhancing hydrological predictions in data-sparse and human-modified regions, addressing hydrology's "cascade of uncertainty", developing improved methods for multivariate prediction, and uncovering causality.