High-resolution simulations, such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM), provide valuable insights into the complex interactions among aerosols, clouds, and precipitation, which are the major contributors to climate change uncertainty. However, due to its exorbitant computational costs, it can only be employed for a limited period and geographical area. To address this, we propose a more cost-effective method powered by emerging machine learning approach -- leveraging high-resolution climate simulation as the oracle and abundant unlabeled data drawn from satellite data -- to better understand the intricate dynamics of the climate system. Our approach involves active learning techniques to predict autoconversion rates, a crucial step in precipitation formation, while significantly reducing the need for a large number of labeled instances. In this study, we present novel methods: custom query strategy fusion for labeling instances, WiFi and MeFi, along with active feature selection based on SHAP, designed to tackle real-world challenges due to its simplicity and practicality in application, specifically focusing on the prediction of autoconversion rates.