Understanding the Role of Adversarial Learning in Precipitation Super-Resolution Through Explainable AI

High-resolution precipitation information is essential for hydrological impact assessment, flood risk analysis, and the characterization of extreme events, yet climate and weather model outputs are typically available at spatial resolutions too coarse to resolve fine-scale variability. Deep-learning-based statistical downscaling has emerged as an effective approach for bridging this resolution gap; however, models trained with pixel-wise objectives often suppress spatial variability and underestimate extremes. Adversarial learning has been shown to improve the realism of downscaled precipitation fields, particularly for extreme events, but the mechanisms through which adversarial objectives influence model behavior remain insufficiently understood. In this study, we investigate how adversarial training modifies the internal representation of precipitation extremes within a super-resolution downscaling framework, using explainable artificial intelligence (XAI) as a diagnostic tool. We employ a unified U-Net architecture trained under two optimization strategies: (i) a deterministic formulation using a pixel-wise mean-squared-error loss, and (ii) an adversarial formulation in which the same U-Net generator is trained jointly with a critic through an adversarial loss. This controlled design isolates the effects of adversarial learning while holding architecture and input information constant. XAI techniques are applied to analyze differences in spatial sensitivity and attribution patterns between the two training regimes, with particular emphasis on extreme precipitation events. Rather than serving as a performance metric, XAI is used to interrogate how adversarial training reshapes the model’s reliance on spatial structure and localized variability. This work highlights the potential of XAI to provide mechanistic insight into generative downscaling models and to support more transparent evaluation of adversarial approaches for extreme precipitation.