Biases in climate models contribute to the high levels of uncertainty in many aspects of climate change related to the atmospheric circulation as the biases project strongly on future changes. Likewise biases also have detrimental effects on predictive skill for dynamically driven fields at prediction time scales of days to decades. Despite significant advances in both weather and climate models large biases persist. Reducing these biases is critically important, especially for constraining regional impacts and effects of present and future change. This session seeks submissions that aim to improve physical understanding of the drivers of weather and climate model biases. These could include evaluation studies, process studies, idealized modeling studies as well as investigations with strong observational components. Studies that include investigations of so-called “emergent constraints” (i.e. relationships between present day model biases and the climate change signal) are especially welcome. Potential participants are also not limited to atmospheric models exclusively as biases arising from coupled processes are also of interest.