Accurate evaluation of daily reference evapotranspiration (ETref) is essential for effective water resource management and drought mitigation, particularly in arid climates. However, developing countries frequently lack the necessary infrastructure for precise ETref assessment. Recent advancements have introduced various black box machine learning (ML) models, including the Adaptive Neuro-Fuzzy Inference System-Particle Swarm Optimization algorithm (ANF-PSO), Random Forest (RF), and Support Vector Machine (SVM), to predict daily ETref. Despite their effectiveness, these models suffer from a lack of interpretability, raising concerns about biases, fairness, and accountability in decision-making. Additionally, their performance varies significantly across different climatic conditions, limiting their general applicability. To address these challenges, this paper presents DIRECTORS, a novel daily ETref prediction model based on pattern mining. DIRECTORS leverages correlations among meteorological parameters and autonomously extracts climate-specific behavioral patterns without predefined pattern lengths. By utilizing these patterns and recent station behavior, DIRECTORS forecasts macroscopic daily ETref values and further refines these predictions using RF based on identified similar patterns. This innovative approach offers distinctive insights and solutions to the limitations of traditional ML models in daily ETref prediction. Extensive evaluation demonstrates DIRECTORS' effectiveness and its potential to significantly enhance predictive accuracy, making it a valuable tool for water resource management and planning in varying environmental conditions.