Generating High-Resolution Regional Precipitation Using Conditional
  Diffusion Model
Naufal Shidqi Chaeyoon Jeong Sungwon Park Elke Zeller Arjun Babu Nellikkattil Karandeep Singh
Climate downscaling is a crucial technique within climate research, serving
to project low-resolution (LR) climate data to higher resolutions (HR).
Previous research has demonstrated the effectiveness of deep learning for
downscaling tasks. However, most deep learning models for climate downscaling
may not perform optimally for high scaling factors (i.e., 4x, 8x) due to their
limited ability to capture the intricate details required for generating HR
climate data. Furthermore, climate data behaves differently from image data,
necessitating a nuanced approach when employing deep generative models. In
response to these challenges, this paper presents a deep generative model for
downscaling climate data, specifically precipitation on a regional scale. We
employ a denoising diffusion probabilistic model (DDPM) conditioned on multiple
LR climate variables. The proposed model is evaluated using precipitation data
from the Community Earth System Model (CESM) v1.2.2 simulation. Our results
demonstrate significant improvements over existing baselines, underscoring the
effectiveness of the conditional diffusion model in downscaling climate data.