I gained my first research experience in climate modeling during my Bachelor and Master studies at ETH Zurich. Two internships at MeteoSwiss and Meteomedia (today MeteoGroup) further provided me with valuable insight into public and private weather services. My fascination for large-scale atmospheric dynamics and predictability was particularly fostered during my PhD, which I received from ETH Zurich in 2017, with the title “Potential vorticity diagnostics to quantify effects of latent heating in extratropical cyclones: methodology and application to idealized climate change simulations”. In this project, we drew upon the well-established concept of potential vorticity modification through cloud-condensational processes to study the role of latent heating for extratropical cyclones in future, warmer and moister, climates. During my first short postdoc project at ETH Zurich, I started working in the exciting research field on sub-seasonal atmospheric predictability (approximately 15 to 60 days), which requires combining knowledge about processes on both weather and climate scales in a seamless way. More specifically, we investigated the role of the stratosphere for predicting energy-industry-relevant surface weather in Europe on monthly timescales. Since 2018, I have been continuing my work on sub-seasonal predictability as a research associate in the SPREADOUT group here at KIT. My research focus is on the representation and predictability of large-scale weather regimes in operational sub-seasonal (re)forecasts from the S2S prediction project database and its sensitivity to synoptic processes as well as processes varying on seasonal timescales such as the stratospheric polar vortex. Last but not least, I am involved in different projects on the application of sub-seasonal weather forecasts by end users such as the energy industry or the public health sector.