WRF-ML Coupling Toolkit: Machine Learning-Based Optimization Code (XGBoost/RF/LSTM) for Extreme Event Simulation in Wanzhou Section of Three Gorges Reservoir Area

This dataset provides Jupyter Notebook (.ipynb) scripts for post-processing optimization of WRF model outputs. It integrates XGBoost, Random Forest (RF), and Long Short-Term Memory (LSTM) algorithms to correct systematic biases in high-resolution simulations (focusing on extreme events: heavy rainfall, heatwaves) over the complex terrain of Wanzhou, Three Gorges Reservoir Area. The code enables intelligent calibration of meteorological variables (temperature, precipitation, wind speed) and supports climate resilience studies in the Yangtze River Basin.