Description
This study presents a comparative evaluation of the LightGBM and XGBoost algorithms for the task of next-day (J+1) daily precipitation forecasting. The analysis utilizes a comprehensive dataset of 8,765 daily meteorological observations spanning the entire continental United States over a 24-year period (2000–2023). The research focuses on assessing predictive performance in relation to seasonal climatic variables and evaluates model robustness against interannual variability. With a pedagogical objective , the study aims to identify the most influential climatic determinants for short-term hydrometeorological prediction.
🎯 The detailed methodology and results can be accessed through this link:
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Abdi-Basid ADAN, 2025
