Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …

Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

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 …

Projecting climate change is a generalization problem: We extrapolate the recent past using
physical models across past, present, and future climates. Current climate models require …

Taking advantage of a large ensemble of Convection Permitting‐Regional Climate Models
on a pan‐Alpine domain and of an object‐oriented dedicated analysis, this study aims to …

High‐resolution and accurate prediction of near‐surface weather parameters based on
numerical weather prediction (NWP) models is essential for many downstream and real …

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 …

Regional climate models (RCMs) have a high computational cost due to their higher spatial
resolution compared to global climate models (GCMs). Therefore, various downscaling …

In an era of frequent extreme weather and global warming, obtaining precise, fine-grained
near-surface weather forecasts is increasingly essential for human activities. Downscaling …

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 …