Large-domain hydrologic modeling and prediction capabilities are central to national hydrometeorological services worldwide. Such services monitor, predict, and project future hydrometeorological conditions from everyday variability to extreme events. At their heart, these capabilities depend on hydrologic datasets, methods and models, as well as their ability to quantify uncertainty, especially for predictions and projections.

We invite contributions that advance large-domain hydrometeorological modeling and prediction capabilities, particularly with operational relevance. We encourage submissions related to:

• Development and evaluation of large-domain meteorological and hydrologic datasets.
• Applications of large-domain and large-sample datasets for hydrologic modeling, prediction, and projection.
• Development of applied efforts from process-based to artificial intelligence (AI)-driven methods (e.g., machine learning, deep learning) for data processing, modeling, and prediction.
• Uncertainty quantification and the use of probabilistic/ensemble datasets and modeling methods.
• Real-world perspectives on evolving applied hydrometeorological modeling and prediction capacities, since the advent of usable AI.