Climate models are essential for predicting future climate conditions and understanding climate change. This review article discusses the importance of optimizing climate models using process knowledge, resolution, and artificial intelligence (AI). The central challenge in climate modeling is accurately representing small-scale processes that are important for the climate system, such as turbulence and cloud formation. These processes cannot be explicitly resolved in current models, so parameterizations are used to represent them. The article proposes a balanced approach that combines traditional process-based parameterizations with AI-based methods to model subgrid-scale processes. AI can be used to derive data-driven closure functions from observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing the resolution of models can help resolve more small-scale processes, improving the accuracy and interpretability of climate predictions. However, current feasible horizontal resolutions are limited to about 10 km because higher resolutions would impede the creation of ensembles needed for model calibration and uncertainty quantification. By combining decades of scientific development with advanced AI techniques, the approach aims to significantly improve the accuracy, interpretability, and trustworthiness of climate predictions. The article also discusses the evolution of climate models over the past 20 years, highlighting improvements in simulating climate statistics such as TOA radiative energy fluxes and precipitation rates. It emphasizes the importance of a balanced approach that incorporates progress along each of the three dimensions: process-based parameterizations, resolution, and AI-based models. The article concludes that while there have been improvements in climate models, there is still a need for further development to achieve more accurate and reliable climate predictions.Climate models are essential for predicting future climate conditions and understanding climate change. This review article discusses the importance of optimizing climate models using process knowledge, resolution, and artificial intelligence (AI). The central challenge in climate modeling is accurately representing small-scale processes that are important for the climate system, such as turbulence and cloud formation. These processes cannot be explicitly resolved in current models, so parameterizations are used to represent them. The article proposes a balanced approach that combines traditional process-based parameterizations with AI-based methods to model subgrid-scale processes. AI can be used to derive data-driven closure functions from observational and simulated data, integrated within parameterizations that encode system knowledge and conservation laws. Increasing the resolution of models can help resolve more small-scale processes, improving the accuracy and interpretability of climate predictions. However, current feasible horizontal resolutions are limited to about 10 km because higher resolutions would impede the creation of ensembles needed for model calibration and uncertainty quantification. By combining decades of scientific development with advanced AI techniques, the approach aims to significantly improve the accuracy, interpretability, and trustworthiness of climate predictions. The article also discusses the evolution of climate models over the past 20 years, highlighting improvements in simulating climate statistics such as TOA radiative energy fluxes and precipitation rates. It emphasizes the importance of a balanced approach that incorporates progress along each of the three dimensions: process-based parameterizations, resolution, and AI-based models. The article concludes that while there have been improvements in climate models, there is still a need for further development to achieve more accurate and reliable climate predictions.