A Machine Learning Framework for Long-term Precipitation Prediction Using Gradient Boosting and Ensemble Techniques

Abstract

Long-term precipitation prediction remains a challenging task due to the nonlinear and dynamic interactions among meteorological variables, particularly in regions with complex climatic conditions. This study proposes a Machine Learning framework based on XGBoost combined with temporal feature engineering for predicting rainfall, snowfall, and total precipitation in North Macedonia. Given the limitations of traditional numerical weather prediction models and the lack of region-specific studies focused on the Balkan climate, this research investigates whether advanced supervised learning algorithms can improve the accuracy and stability of precipitation forecasting.A comprehensive dataset covering the period from 2000 to 2025 was obtained from Visual Crossing, followed by extensive preprocessing and feature engineering. The engineered features include lag variables, rolling statistics, seasonal indicators, and interaction terms to better capture temporal dependencies and complex relationships in the data.Three predictive models were developed: a rainfall classifier, a snowfall classifier, and a general precipitation classifier. The primary algorithm employed was XGBoost, while the snowfall prediction model utilized an ensemble stacking approach combining XGBoost, Random Forest, and LightGBM to address class imbalance and enhance model robustness. Model validation was conducted using a TimeSeriesSplit strategy with temporal gaps to prevent data leakage.The results demonstrate significant performance improvements using gradient boosting-based methods. The snowfall model achieved an accuracy ranging from 90% to 97%, rainfall prediction reached 81% to 84%, and the general precipitation model achieved 80% to 81% accuracy. Feature importance analysis identified temperature, humidity, atmospheric pressure patterns, and cloud cover as the most influential predictors. These findings confirm that machine learning provides a reliable and effective approach for long-term precipitation forecasting in the region, with potential applications in agriculture, transportation, and environmental planning.