  Downscaling techniques are one of the most prominent applications of Deep
Learning (DL) in Earth System Modeling. A robust DL downscaling model can
generate high-resolution fields from coarse-scale numerical model simulations,
saving the timely and resourceful applications of regional/local models.
Additionally, generative DL models have the potential to provide uncertainty
information, by generating ensemble-like scenario pools, a task that is
computationally prohibitive for traditional numerical simulations. In this
study, we apply a Latent Diffusion Model (LDM) to downscale ERA5 data over
Italy up to a resolution of 2 km. The high-resolution target data consists of
results from a high-resolution dynamical downscaling performed with COSMO-CLM.
Our goal is to demonstrate that recent advancements in generative modeling
enable DL-based models to deliver results comparable to those of numerical
dynamical downscaling models, given the same input data (i.e., ERA5 data),
preserving the realism of fine-scale features and flow characteristics. The
training and testing database consists of hourly data from 2000 to 2020. The
target variables of this study are 2-m temperature and 10-m horizontal wind
components. A selection of predictors from ERA5 is used as input to the LDM,
and a residual approach against a reference UNET is leveraged in applying the
LDM. The performance of the generative LDM is compared with reference baselines
of increasing complexity: quadratic interpolation of ERA5, a UNET, and a
Generative Adversarial Network (GAN) built on the same reference UNET. Results
highlight the improvements introduced by the LDM architecture and the residual
approach over these baselines. The models are evaluated on a yearly test
dataset, assessing the models' performance through deterministic metrics,
spatial distribution of errors, and reconstruction of frequency and power
spectra distributions.
