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Hydrological models are widely used to support water resources planning, flood and drought assessment, watershed management, and climate change impact analysis. The credibility of such applications depends strongly on how model performance is evaluated against observed data. Numerous statistical performance metrics have been proposed for hydrological model evaluation; however, their mathematical formulations, sensitivity to flow regimes, and interpretability differ substantially. As a result, the choice of evaluation criteria can strongly influence conclusions regarding model adequacy and comparative performance. This review synthesizes commonly used hydrological model performance metrics, including the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Percent Bias (PBIAS), Kling-Gupta Efficiency (KGE), RMSE-observations standard deviation ratio (RSR), and Willmott’s index of agreement. For each metric, definitions, formulations, interpretation guidelines, strengths, and limitations are discussed based on classical and recent literature. Particular attention is given to the sensitivity of metrics to high-flow and low-flow conditions, systematic bias, variability, and error magnitude, as well as their suitability for different water resources applications. Comparative analysis highlights that no single metric can adequately capture all aspects of hydrological model performance, and metric-dependent ranking of models is common across studies. The review study emphasizes the importance of multi-criteria evaluation frameworks and application-specific metric selection, especially under non-stationary climatic conditions. Practical recommendations are provided to support transparent, consistent, and meaningful performance evaluation in hydrological modeling and water resources decision-making.