Temporal Downscaling Using Deep Learning for Sub-hourly Time Series

Recent climate change has been linked to more frequent and more intense short-timescale rainfall extremes, increasing exposure to urban pluvial flooding. Because many urban catchments respond within minutes, rainfall information at sub-hourly resolution is often needed for hydrologic analyses. An AI-driven temporal downscaling approach is introduced here to derive 10-minute rainfall series from hourly observations using a conditional diffusion generative model. Rain-gauge observations at Seoul Gwanaksan (#1917), operated by the Korea Forest Service, were used. The record covers the years 2015 through 2024. Paired hourly totals and observed 10-minute series were prepared to examine whether sub-hourly rainfall sequences can be reconstructed from hourly totals while preserving realistic within-hour variability. The feasibility of loss function variation was investigated. The experiments indicate that incorporating distributional and temporal statistics into the objective function can enhance the realism of sub-hourly rainfall structure under hourly constraints. The proposed framework is expected to provide more reliable 10-minute rainfall inputs for urban hydrologic analyses and pluvial-flood–relevant applications in rapid-response catchments.