An integrated framework for quantifying future Australia's sustainability based on drought characteristics

The code implements a grid-based modeling framework for predicting and evaluating precipitation across Australia using multiple CMIP6 climate models. It applies bias correction, Taylor Skill Score (TSS)-based weighting, and machine learning techniques such as Random Forest to create an ensemble prediction. The workflow is developed in Python using libraries like xarray, numpy, scikit-learn, matplotlib, and cartopy, and is executed in Google Colab for scalable processing and visualization.