Validation of climate models is essential to refine their physics and ensure their reliability for decision making. However, current validation methods primarily focus on individual climate variables, neglecting the complex interactions between these factors. This oversight hinders accurate bioclimatic assessments and the development of effective policies. Climate Classification Systems (CCSs) have emerged as a promising tool for addressing this challenge. CCSs were developed in the 19th century to explain the global distribution of plants, but have been given new life in the Earth physics community in the 21st century as a diagnostic tool for Earth System Models (ESMs). CCSs provide a rational and comprehensive zonation of the environment linking climate and biota. This framework is based on quantitative estimates of physical variables such as precipitation, temperature, and soil moisture, and effectively serves as a spatial fingerprint of local atmosphere-land interactions. As multidimensional evaluation metrics, they integrate multiple climate variables, applying clear and consistent rules to generate a single-dimensional output that comprehensively captures the underlying climatic complexity. By merging energy and water balances, the integrated view offered by CCSs can unveil relationships hidden in isolated, one-dimensional performance metrics. They also offer several advantages over single variables due to the high sensitivity of the resulting classes to small changes in input variables and dimensionality reduction. This sensitivity to small biases allows CCSs to detect subtle discrepancies in how models represent key climate features, providing insights for model improvement.