Global Storm Resolving Models (GSRMs) is crucial for understanding extreme weather events under the climate change background. In this study, we optimize Global-Regional Integrated Forecast System (GRIST), which is a unified weather-climate modeling system designed for research and operation, for the next-generation Sunway supercomputer, incorporating AI-enhanced physics suite, OpenMP-based parallelization, and mixed-precision optimizations to enhance both efficiency and performance portability, as well as the unified modeling capability. Our experiments successfully capture significant events during the "23.7" extreme rainfall over northern China influenced by super Typhoon Doksuri, at 1km resolution. Notably, our work scales to 34 million cores, enabling simulation speeds at 491 SDPD (3km) and 181 SDPD (1km).

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society.