https://doi.org/10.1175/AIES-D-25-0093.1

Abdelmoaty, H. M., S. M. Papalexiou, A. Mamalakis, S. Singh, V. Coia, M. Hairabedian, P. Szeftel, and P. Grover, 2026: Does Nonstationarity Affect GAN-Based Downscaling? Insights from High-Resolution WRF Simulations over the Canadian Prairies. Artif. Intell. Earth Syst., 5, 250093, https://doi.org/10.1175/AIES-D-25-0093.1 .

Although downscaling models are typically trained on historical data and then applied to future projections, this practice raises concerns about their ability to reproduce nonstationary climate signals. While such limitations are expected for traditional statistical approaches, deep learning models have the potential to learn a more generalizable downscaling relationship, separating the transformation task from the nonstationary signal. In this study, we examine the sensitivity of deep learning–based downscaling to nonstationarity using Wasserstein generative adversarial networks (WGANs) to downscale hourly precipitation from 50 to 4 km over the Canadian Prairies. We trained two models on convection-permitting Weather Research and Forecasting (WRF) simulations: one on a historical period (2000–15) and another on a future period (2086–2100). Four setups were considered: hh (trained/tested on historical), ff (trained/tested on future), fh (trained on future/tested on historical), and hf (trained on historical/tested on future). Within-climate models (hh and ff) reproduced spatial heterogeneity more effectively, with better spatial correlations and realistic variability, while all four reproduced temporal autocorrelation. Extremes remain a challenge, yet with negative biases, in terms of hourly precipitation upper percentiles (75th, 95th, 99th, and 99.9th) and daily maxima. The hh and ff cases performed best; the fh case benefited from exposure to stronger extremes during training, while hf performed poorest due to weak generalization. Importantly, however, cross-climate model biases (hf and fh) were not substantially larger than within-climate ones. These findings highlight the challenge of training on stationary data for nonstationary climates but also demonstrate the potential of WGANs to mitigate such limitations.

https://journals.ametsoc.org/downloadpdf/view/journals/aies/5/2/AIES-D-25-0093.1.pdf