Adapting to the changing climate requires accurate local climate information,
a computationally challenging problem. Recent studies have used Generative
Adversarial Networks (GANs), a type of deep learning, to learn complex
distributions and downscale climate variables efficiently. Capturing
variability while downscaling is crucial for estimating uncertainty and
characterising extreme events - critical information for climate adaptation.
Since downscaling is an undetermined problem, many fine-scale states are
physically consistent with the coarse-resolution state. To quantify this
ill-posed problem, downscaling techniques should be stochastic, able to sample
realisations from a high-resolution distribution conditioned on low-resolution
input. Previous stochastic downscaling attempts have found substantial
underdispersion, with models failing to represent the full distribution. We
propose approaches to improve the stochastic calibration of GANs in three ways:
a) injecting noise inside the network, b) adjusting the training process to
explicitly account for the stochasticity, and c) using a probabilistic loss
metric. We tested our models first on a synthetic dataset with known
distributional properties, and then on a realistic downscaling scenario,
predicting high-resolution wind components from low-resolution climate
covariates. Injecting noise, on its own, substantially improved the quality of
conditional and full distributions in tests with synthetic data, but performed
less well for wind field downscaling, where models remained underdispersed. For
wind downscaling, we found that adjusting the training method and including the
probabilistic loss improved calibration. The best model, with all three
changes, showed much improved skill at capturing the full variability of the
high-resolution distribution and thus at characterising extremes.

应对气候变化需要准确的本地气候信息，最近的研究使用生成对抗网络 (GANs) 这种深度学习方法，以高效地学习复杂分布和缩放气候变量。为了估计不确定性和表征极端事件的关键信息，捕捉缩放过程中的变异性至关重要。因为缩放是一个不确定问题，许多细致的状态在物理上与粗粒度状态一致。为了量化这个状态不明确的问题，缩放技术应该是随机的，并能够在低精度输入条件下从高精度分布中采样。我们提出了三种改进 GANs 随机校准的方法：a）在网络内部注入噪声，b）调整训练过程以明确考虑随机性，c）使用概率损失度量。我们首先在具有已知分布特性的合成数据集上测试了我们的模型，然后在实际缩放场景中进行了测试，从低分辨率气候协变量预测高分辨率风场分量。仅仅注入噪声即大大改善了在合成数据测试中的条件和完整分布的质量，但对风场缩放表现不佳，在这种情况下模型仍然缺乏离散度。对于风场缩放，我们发现调整训练方法并增加概率损失可以改善校准。最佳模型通过三种改变在捕捉高精度分布的完整变异性方面显示出了显著提升的技能，从而更好地描述极端情况。