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<p>Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial intelligence (AI) enable high-resolution, probabilistic, and computationally efficient approaches. This paper provides a perspective on the evolution from classical to AI-driven and hybrid downscaling approaches, assesses key challenges related to interpretability, uncertainty, data availability, and computational requirements, and outlines physically constrained and generative frameworks that support decision-making across sectors.</p>

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New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月4日

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  • Kwok Pan Chun,
  • Leonardo Aragão,
  • Matías Ezequiel Olmo,
  • Viet Dung Nguyen,
  • Christoforus Bayu Risanto,
  • Maria Laura Bettolli,
  • Yasemin Ezber,
  • Emir Toker,
  • Konstantinos V. Varotsos

摘要

Statistical downscaling translates coarse-resolution climate model output into locally relevant information for climate services and impact assessment. Recent advances in artificial intelligence (AI) enable high-resolution, probabilistic, and computationally efficient approaches. This paper provides a perspective on the evolution from classical to AI-driven and hybrid downscaling approaches, assesses key challenges related to interpretability, uncertainty, data availability, and computational requirements, and outlines physically constrained and generative frameworks that support decision-making across sectors.