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<strong class="journal-contentHeaderColor">Abstract.</strong> High-resolution precipitation information is essential for hydrologic modeling, flood forecasting, and climate-risk assessment, yet global weather and climate models operate at spatial resolutions too coarse to resolve storm structure, intermittency, and extremes. Deep-learning-based statistical downscaling provides a computationally efficient alternative to dynamical downscaling, but deterministic convolutional neural networks often yield overly smooth predictions and underestimate fine-scale variability and extreme events. Generative deep-learning models, including generative adversarial networks and diffusion models, offer a promising alternative by enabling stochastic downscaling and explicit representation of uncertainty. This study presents a systematic, hydrologically oriented comparison of three representative deep-learning frameworks for precipitation super-resolution: a convolutional U-NET, a conditional Wasserstein GAN (WGAN), and a conditional denoising diffusion probabilistic model (DDPM). Using a perfect-model experimental design based on ERA5-Land precipitation over distinct hydroclimatic regions of the United States, we evaluate performance under 8-times (8×) and 16-times (16×) downscaling tasks within a unified training and evaluation framework. Models are evaluated using diagnostics that examine precipitation distributions, wet–dry occurrence, extremes, spatial structure, storm morphology, mass consistency, ensemble variability, and computational cost. All three models preserve aggregate rainfall mass despite the absence of explicit physical constraints. Differences arise primarily at fine spatial scales and in the representation of extremes, spatial dependence, and uncertainty. U-NET provides stable and computationally efficient predictions but smooths small-scale variability. WGAN improves fine-scale structure and heavy-tail behavior at the expense of increased noise. The DDPM yields physically coherent ensemble members and an explicit representation of uncertainty, at a substantially higher computational cost.