Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …

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

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 …

Data mining and analysis are critical for preventing or mitigating natural hazards. However,
data availability in natural hazard analysis is experiencing unprecedented challenges due to …

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 …

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 …

The generation of electric energy through renewable sources, such as solar photovoltaic
(PV) systems, has emerged as one solution to the climate change crisis. To avoid …

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

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 …