GT4Py is a Python framework for weather and climate applications simplifying the development and maintenance of high-performance codes in prototyping and production environments. GT4Py separates model development from hardware architecture dependent optimizations, instead of intermixing both together in source code, as regularly done in lower-level languages like Fortran, C, or C++. Domain scientists focus solely on numerical modeling using a declarative embedded domain specific language supporting common computational patterns of dynamical cores and physical parametrizations. An optimizing toolchain then transforms this high-level representation into a finely-tuned implementation for the target hardware architecture. This separation of concerns allows performance engineers to implement new optimizations or support new hardware architectures without requiring changes to the application, increasing productivity for domain scientists and performance engineers alike. We will present recent developments in the project: support for interactive debugging, new compiler passes that optimize data-movement, an improved frontend with support for high-level constructs, and new backends connecting GT4Py with existing HPC frameworks (DaCe, Jax). We further showcase performance results of two atmospheric models (ICON, FVM) on the new NVIDIA Grace-Hopper nodes of the CSCS Alps supercomputer.