Climate change is expected to intensify droughts, thereby increasing the need for reliable predictive tools. In this study, one-month-ahead forecasts of the Palmer Z-Index were generated using long-term monthly data from two meteorological stations (17112 Çanakkale and 18084 Biga) located in the Troy region. The input features included current and lagged meteorological variables, multi-month rolling statistics, and seasonal encodings. Eight machine learning models, including linear and ensemble tree-based approaches, were evaluated using time series cross-validation. Drought events were defined based on Palmer Z-Index and standardized drought indicators, and model performance was assessed using commonly adopted accuracy and detection measures. Shapley Additive Explanations (SHAP) analysis was used to quantify the feature contributions. Gradient Boosting achieved the highest predictive accuracy at the main station, while XGBoost and CatBoost also performed strongly. High accuracy was maintained at the second station, demonstrating the spatial robustness of the model. The machine learning-predicted Palmer Z-Index values showed strong agreement with observed hydrological drought conditions; severe drought events were detected with high confidence and low false alarm rates. SHAP results identified precipitation inputs as the most dominant driver of Z-Index variability. Overall, the findings suggest that ML-based models can provide timely and interpretable forecasts for operational drought early warning systems. Nonetheless, further research is needed to test the generalizability of these findings under different climate regimes and data conditions.
Mucan et al. (Mon,) studied this question.