The article from ESS Open Archive discusses the innovative approach of 'online learning' for a machine learning (ML) cloud fraction closure within a hybrid Earth System Model (ESM). This research is significant as Earth System Models are crucial for understanding and predicting climate change, yet they often face systematic errors and uncertainties, particularly in representing small-scale processes like clouds and convection. Clouds play a vital role in Earth's radiation balance, and their accurate representation, known as cloud fractional cover (CFC), is a major challenge in climate modeling. The use of machine learning offers a promising avenue to improve these projections. The concept of a 'hybrid' ESM involves integrating advanced ML techniques with traditional physical models to enhance accuracy and reduce these inherent systematic errors, aiming for faster and more precise climate predictions. 'Online learning' is a key aspect highlighted in this research. Unlike offline-trained ML components that can lead to instability and drift when coupled with climate models, online learning allows the ML component to be trained or refined continuously during the model's execution. This approach directly targets climate variables of interest, such as vertical profiles of entropy and liquid water path, leading to more robust and stable machine learning parameterizations. This field of research is actively pursued, with similar studies demonstrating how ML can improve cloud cover parameterizations and potentially lead to better predictions of precipitation extremes. ESS Open Archive, the source, is a credible open-access preprint server for Earth and Space Science, supported by major professional societies like the American Geophysical Union (AGU). It facilitates the rapid dissemination of early research outputs, though content is evaluated by an editorial board rather than undergoing formal peer review prior to posting. This ensures that important scientific findings, like those in climate modeling, are shared promptly with the global scientific community. This research is globally relevant, as improved climate models have profound implications for environmental monitoring, disaster surveillance, and climate analysis worldwide, including for countries like India which are highly susceptible to climate change impacts.