Hydrologic applications demand predictions across a wide range of spatial and temporal scales, yet our process understanding is often constrained to specific scales. This discrepancy introduces fundamental challenges due to nonlinear dynamics, multi-scale heterogeneity, and emergent behaviors at scale transitions. Consequently, hydrologic models designed for a given scale frequently rely on complex closure schemes and ad hoc parameterizations to represent unresolved sub-scale processes. This session invites contributions that tackle the long-standing scaling challenge in hydrology and earth system science. We particularly encourage studies that integrate information across scales—spatial or temporal—and explore the implications of scale mismatches for modeling, data analysis, and scientific inference. Approaches may include theoretical or empirical work, process-based or conceptual modeling, data assimilation techniques, and machine learning, including physics-informed models. We especially welcome innovative, exploratory efforts and insights from unsuccessful attempts that reveal structural limitations or inspire future directions.