Starting in 2020, a new modeling initiative has been launched as a joint project between climate modeling institutes and the Deutscher Wetterdienst. The initiative integrates NWP, climate predictions, climate projections, and atmospheric composition modeling based on the ICON framework and targets a unified treatment of the respective subgrid-scale parameterizations. It aims at the development of coupled model configurations of ICON to conduct operational weather and ocean forecasts for several days, climate predictions with timescales up to 10 years ahead as well as climate projections, and further provides a model baseline for joint research for NWP and climate (Müller et al., 2025).
ICON XPP is an outcome of this initiative and provides the baseline for the next generation of climate predictions and projections, and global climate research (where XPP stands for eXtended Predictions and Projections). ICON XPP comprises the atmospheric component as used for the numerical weather prediction (ICON NWP), the ICON ocean model, the land surface component ICON Land, and a data assimilation system, all adjusted to an Earth System model for pursuing climate research and operational climate forecasting.
Here, I give a survey, from the strategic decision made for unifying ICON for NWP and climate, to a first assessment of the global climate in baseline experiments of ICON XPP, to future directions of model developments such as the upcoming national contribution to Coupled Model Intercomparison Project (CMIP7) and an effort for an ultra-high-resolution workhorse configuration (13km atm; 5km oce).
 
Müller, W. A., and Coauthors, 2025: ICON: Towards vertically integrated model configurations for numerical weather prediction, climate predictions and projections. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-24-0042.1, in press.

(1) Significance and Robustness of Climate Change Signals for Extreme Indices over Germany in a Convection-permitting Climate Model Ensemble (2) tbd (3) Extreme fire weather in a changing climate: Projections for Europe based on bias adjusted EURO-CORDEX simulations (4) Near-real-time probabilistic Hail Detection based on polarimetric radar quantities and environmental conditions using machine learning methods

Extreme heat has wide-ranging impacts on human health, performance, and well-being. Outdoor thermal exposure is shaped by the complex interplay between urban infrastructure and micro- to global-scale climates—interactions that must be understood to effectively mitigate heat in cities. Phoenix, Arizona, with its hot, dry climate, serves as a living laboratory where cooling strategies can be tested in real-world urban environments. This presentation highlights recent research from the SHaDE Lab at Arizona State University on heat mitigation strategies such as trees, engineered shade, and cool pavements. These solutions are evaluated through a combination of field measurements, microclimate simulations, and community-based experiments. Novel sensing techniques capture how heat is experienced at the human scale, including MaRTy (a mobile biometeorological platform that can sense how pedestrians experience heat), MaRTinies (low-cost IoT sensors), and the thermal manikin ANDI, which simulates human thermoregulation under heat stress. Complementary modeling approaches help quantify and optimize shade distribution and predict thermal comfort outcomes. Together, these methods provide a comprehensive understanding of urban heat and inform practical strategies for designing cooler, healthier cities.

(1) tbd (2) tbd (3) tbd (4)  Evaluation of AI Weather Models for Sub-seasonal Forecasts