It is a broad consensus among climate scientist that our climate is warming. This is summarized in the Assessment Reports of the Intergovernmental Panel on Climate Change. As outlined by Tim Palmer and Bjorn Stevens in their article  The scientific challenge of understanding and estimating climate change the challenge today is

to inform society about the pace of warming, how this warming plays out regionally, and what it implies for the likelihood of surprises.

A tool to answer these question are

Global storm- and eddy-resolving weather and climate models,

which can explicitly resolve processes such as atmospheric convection and hence simulate, e.g., clouds and precipitation much more realistically. However, in comparison to state-of-the-art climate and weather models, the resolution of these new models needs to be increased by orders of magnitude, and only upcoming exascale supercomputers will be sufficiently powerful to satisfy the computational demands of such models. Numerical weather prediction and climate modelling always have been highly dependent on the available computing power and the ability to produce, store and analyse large amounts of simulated data. Much more computational power is necessary for any increase in the achievable spatial resolution and the completeness and accuracy of physical processes that can be calculated and predicted by the models. Due to the enormous economic importance of weather and climate predictions, these simulations for decades have been routinely run on some of the most powerful supercomputers worldwide. With the transition to exascale computing, operational use of global storm-resolving models, i.e. models based on a very fine underlying mesh that spans the globe with grid spaces of only a few km, will become possible, ultimately allowing to explicitly resolve vertical energy transfers in the atmosphere. This will mark a step change in the quality of weather and climate forecasting. Furthermore, it opens the path for a direct comparison with observational data e.g. from weather satellites.

A day in early February 2020 in a traditional CMIP6 High resolution simulation and in a DYAMOND 2.5 km simulation performed in the context of ESiWACE. The Weather in both simulations is different, as the CMIP simulation is a free-running model that has been running for centuries, while the DYAMOND simulation has been running for a few days only with initial values from the ECMWF weather prediction system.

A day in early February 2020 in a traditional "CMIP6 high resolution" simulation (grid resolution ~80km) compared to a 2.5 km "DYAMOND" simulation performed in the context of ESiWACE. While the CMIP6 model is able to capture the broad concept of clouds in the Carribean region, the DYAMOND model is able to represent the intricacies of the cloud structures, and thus represent the behaviors of different cloud types. This much more detailed representation of the atmospheric circulation will lead to drastically improved climate predictions, once these models can be run for longer simulation periods.

The weather in both simulations is different, as the CMIP simulation is a free-running model that has been running for centuries, while the DYAMOND simulation has been running for a few days only with initial values from the ECMWF weather prediction system.