Comprehensive inter-comparison of generative AI models for super-resolution precipitation downscaling across hydroclimatic regimes

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 intercomparison of three representative deep-learning architectures for precipitation super-resolution, namely a Convolutional U-Net as baseline, a conditional Wasserstein GAN (WGAN), and a conditional Denoising Diffusion Probabilistic Model (DDPM). Using a perfect-model experimental design based on ERA5-Land precipitation fields over climatologically distinct regions of the United States, models are trained over the Central Plains and Northwest domains and evaluated over an independent Northeast test domain under 8× and 16× downscaling factors, providing a stringent test of cross-regional generalization. Evaluation diagnostics span precipitation distributions, wet–dry occurrence, extremes, spatial autocorrelation, spectral structure, and ensemble-based uncertainty quantification. All three models preserve large-scale precipitation organization, with differences emerging primarily at fine spatial scales and in the representation of extremes and spatial dependence. U-Net provides stable and computationally efficient predictions but consistently smooths fine-scale variability and suppresses extreme precipitation. WGAN improves distributional fidelity and heavy-tail behavior with comparatively modest computational overhead. DDPM yields the most physically coherent spatial structure and natural ensemble diversity for uncertainty quantification, at a substantially higher computational cost. Analysis of seed-based variability further reveals that training uncertainty dominates over stochastic generation variability, underscoring the need for multi-seed evaluation in generative downscaling systems.