Deep learning has emerged as a promising tool for precipitation downscaling. However, current models rely on likelihood-based loss functions to properly model the precipitation distribution, leading to spatially inconsistent projections when sampling. This work explores a novel approach by fusing the strengths of likelihood-based and adversarial losses used in generative models. As a result, we propose a likelihood-based generative approach for precipitation downscaling, leveraging the benefits of both methods.

翻译：深度学习已成为降水降尺度领域一种前景广阔的工具。然而，现有模型依赖基于似然的损失函数来准确建模降水分布，这导致在采样时会产生空间不一致的预测结果。本研究探索了一种新颖方法，通过融合生成式模型中基于似然的损失与对抗性损失的优势，提出了一种用于降水降尺度的基于似然的生成式方法，从而兼具两种方法的优点。