Comparative study of machine learning-based rainfall prediction in tropical and temperate climates

Reliable rainfall prediction is essential for effective climate adaptation yet remains challenging due to complex atmospheric interactions that vary across regions. This study investigates next-day rainfall predictability in tropical and temperate climates using daily atmospheric data—including pressure, temperature, dew point, relative humidity, wind speed, and wind direction—collected from topographically similar sites in Alor Setar (tropical) and Vercelli, Williams, and Ashburton (temperate) between 2012 and 2015. Logistic regression and random forest models were used to predict rainfall occurrence as a binary outcome. Key variables were identified using Wald’s statistics and p-values in the logistic regression models, while the random forest models relied on mean decrease accuracy for ranking variable importance. The results reveal that rainfall in temperate climates is significantly more predictable than in tropical regions, with the Williams model demonstrating the highest accuracy. Atmospheric pressure consistently emerged as the dominant predictor in temperate regions but was not significant in the tropical model, reflecting the greater atmospheric variability and complexity in tropical rainfall mechanisms. Crucially, the study highlights that as global warming continues to alter temperate climate patterns—bringing increased variability and more convective rainfall—these regions may experience the same predictive uncertainties currently observed in tropical climates. These findings underscore the urgency of developing robust, climate-specific rainfall prediction models that account for changing atmospheric dynamics, with critical implications for weather forecasting, disaster preparedness, and climate resilience planning.