We present the first digital twin framework that operationalizes the production of multi-decadal, global climate projections at kilometre-scale resolution, developed within the European Union’s Destination Earth initiative. Using three coupled Earth system models and selected impact-sector applications, we have built end-to-end workflows for both regular and on-demand climate projections on two EuroHPC supercomputers, LUMI and MareNostrum5. These workflows produced the first-ever multi-decadal simulations at 5 km resolution across all major Earth system components, using the same output parameters and grid, and achieving a production throughput of 0.6 simulated years per day and a climate data portfolio of 6.6 petabytes. We demonstrate the scalability of two of these Earth system models across both CPU and GPU-based systems at global resolutions up to 1 km, across atmosphere, ocean, land, and sea-ice, and report record-breaking full-machine performance on LUMI and MareNostrum5 of up to 97 simulated days per day at 1 km resolution.

High level hardware simulation and modeling techniques matured significantly over the last years and have become more and more important in practice, e.g., in the industrial hardware development and automotive domain. Yet, there are many other challenging application areas such as numerical solvers for environmental or disaster prediction problems, e.g., tsunami and storm surge simulations, that could greatly profit from accurate and efficient hardware simulation. Such applications rely on complex mathematical models that are discretized using suitable numerical methods, and require a close collaboration between mathematicians and computer scientists to attain desired computational performance on current micro architectures and code parallelization techniques to produce accurate simulation results as fast as possible. This complex and detailed simulation requires a lot of time during preparation and execution. Especially the execution on non-standard or new hardware may be challenging and potentially error prone. In this paper, we focus on a high level simulation approach for determining accurate runtimes of applications using instruction accurate modeling and simulation. We extend the basic instruction accurate simulation technology from OVP using cache models in conjunction with a statistical cost function, which enables high precision and significantly better runtime predictions compared to the pure instruction accurate approach.