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    Item type:Publication, Access status: with files ,
    Does nonstationarity affect GAN-based downscaling? Insights from high-resolution WRF simulations over the Canadian prairies
    (Society for Industrial & Applied Mathematics (SIAM), 2026-04-20)
    Abdelmoaty, Hebatallah M.
    ;
    Papalexiou, Simon Michael
    ;
    Mamalakis, Antonios
    ;
    Singh, Shivam
    ;
    Coia, Vincenzo
    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.
    Publicationtype: Journal Article
      5  2
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    Item type:Publication, Access status: with files ,
    Generative adversarial networks for downscaling hourly precipitation in the canadian prairies
    (Wiley, 2025-10-24)
    Abdelmoaty, Hebatallah M.
    ;
    Papalexiou, Simon Michael
    ;
    Mamalakis, Antonios
    ;
    Singh, Shivam
    ;
    Coia, Vincenzo
    Developing robust downscaling methods is essential for maximizing the applicability of climate model outputs in engineering design and climate mitigation, particularly in a changing climate. This study evaluates four deep learning model configurations for downscaling, focusing on their structure, functionality, and ability to capture localized convective events in the Canadian prairies. These model configurations aim to downscale coarse‐resolution climate model outputs (∼200 km) to the finer spatial resolution of regional climate models (∼50 km) for hourly precipitation. We introduce advanced metrics to assess the fidelity of precipitation downscaling, examining both marginal statistics and spatiotemporal dependencies. A U‐Network (UNET) captures spatial and temporal dependencies efficiently while three generative adversarial networks (GANs) configurations incorporate a critic network to enhance the realism of generated fields. The study also evaluates the effects of a thresholding layer to constrain precipitation values and a convolution long short‐term memory layer in the GAN critic to better capture temporal dependencies. Results show that all four model configurations effectively capture spatial dependencies, with the simplest GAN architecture outperforming others in preserving temporal dependencies. Latitudinal correlations are better preserved than longitudinal across all models. While UNET produces overly smoothed fields, GANs generate more detailed outputs when downscaling Coupled Model Intercomparison Project phase 6 projections. By optimizing deep learning models for this region, the study provides key insights into future precipitation trends, enabling the identification of localized storms. These findings are critical for improving infrastructure resilience across catchments in the prairies.
    Publicationtype: Journal Article
      30  21