Climate variability and weather phenomena can cause extremes and pose significant risk to
society and ecosystems, making continued advances in our physical understanding of such …

As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …

Downscaling methods are critical in efficiently generating high‐resolution atmospheric data.
However, state‐of‐the‐art statistical or dynamical downscaling techniques either suffer from …

Global climate models (GCMs) are commonly downscaled to understand future local climate
change. The high computational cost of regional climate models (RCMs) limits how many …

Deep learning (DL) has emerged as a promising tool to downscale climate projections at
regional‐to‐local scales from large‐scale atmospheric fields following the perfect‐prognosis …

Providing reliable information on climate change at local scale remains a challenge of first
importance for impact studies and policymakers. Here, we propose a novel hybrid …

Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …

High resolution regional climate models (RCM) are necessary to capture local precipitation
but are too expensive to fully explore the uncertainties associated with future projections. To …

The analysis of the near-surface air temperature is vital for many applications such as urban
heat islands and climate change studies. In particular, extreme weather events are typically …

Climate model outputs are commonly corrected using statistical univariate bias correction
methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to …