Harilal, NidhinNidhinHarilalSingh, MayankMayankSinghBhatia, UditUditBhatia2025-08-312025-08-312021-01-0110.1109/ACCESS.2021.30575002-s2.0-85106782051https://d8.irins.org/handle/IITG2025/25566Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and natural systems, including critical infrastructures and ecosystems. While impact assessment and adaptation planning rely on high-resolution projections (typically in the order of a few kilometers), state-of-the-art Earth System Models (ESMs) are available at spatial resolutions of few hundreds of kilometers. Current solutions to obtain high-resolution projections of ESMs include downscaling approaches that consider the information at a coarse-scale to make predictions at local scales. Complex and non-linear interdependence among local climate variables (e.g., temperature and precipitation) and large-scale predictors (e.g., pressure fields) motivate the use of neural network-based super-resolution architectures. In this work, we present auxiliary variables informed spatio-temporal neural architecture for statistical downscaling. The current study performs daily downscaling of precipitation variable from an ESM output at 1.15 degrees (115 km) to 1/4 degrees (25 km) over one of the most climatically diversified countries, India. We showcase significant improvement gain against two popular state-of-the-art baselines with a better ability to predict statistics of extreme events. To facilitate reproducible research, we make available all the codes, processed datasets, and trained models in the public domain.trueClimate statistical downscaling | daily precipitation | deep learning | LSTMs | recurrent neural networks | super-resolutionArticlehttps://ieeexplore.ieee.org/ielx7/6287639/9312710/09348885.pdf2169353625208-252182021309348885arJournal26WOS:000617741700001