Global climate models provide crucial insights but lack local precision, limiting their practical application for regional planning and adaptation strategies. This poster presents a Python-based framework for climate model downscaling, using deep learning to bridge the gap between global and local predictions.

The approach leverages PyTorch's neural network capabilities and Python's scientific computing stack for processing complex climate data. The framework combines xarray for multi-dimensional data handling, dask for distributed computing, and custom PyTorch layers optimized for atmospheric pattern recognition. Through MLflow's experiment tracking and Panel's interactive visualizations, we will demonstrate how different neural architectures affect prediction accuracy across various regions and climate variables. This downscaling approach significantly improves prediction accuracy, reducing the Root Mean Square Error (RMSE) by 40-65% compared to traditional statistical methods, with correlation coefficients (r²) improving significantly for temperature predictions in complex terrain regions.

By integrating domain-specific libraries like metpy with NumPy's computational abilities, we will showcase how Python enables scalable climate science . We will be showcasing live notebooks showing how machine learning improves climate predictions. The implementation demonstrates how Python makes it possible to process large amounts of climate data efficiently and create accurate local forecasts.