Accurate day-ahead solar irradiance forecasting is essential for photovoltaic integration, reserve scheduling, and energy storage operation. However, operational Numerical Weather Prediction (NWP) products are commonly available at hourly resolution, which limits their ability to represent sub-hourly irradiance ramps and cloud-induced fluctuations relevant to power system operation. To address this limitation, this study proposes Shape-Prior conditional Flow Matching (SPFlow), a probabilistic temporal-downscaling framework that converts hourly NWP forecasts into 15-min irradiance trajectories. SPFlow first employs a deterministic SPTransformer to construct a physically informed high-resolution shape prior from coarse NWP forecasts, satellite-derived meteorological variables, clear-sky irradiance, and site information. Conditioned on this informative prior, a conditional flow-matching model probabilistically refines the deterministic trajectory to represent unresolved sub-hourly variability and forecast uncertainty. Experiments on the SURFRAD dataset show that SPFlow consistently outperforms statistical, deterministic deep-learning, and probabilistic generative baselines. It reduces the continuous ranked probability score by up to 10.9% relative to the strongest probabilistic baseline, improves sub-hourly ramp representation, produces prediction intervals with near-nominal empirical coverage, and demonstrates promising zero-shot transferability to unseen locations. These results indicate that combining physically informed shape-prior construction with conditional flow-based probabilistic refinement provides an effective approach for converting coarse NWP forecasts into operationally useful sub-hourly probabilistic solar forecasts.