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Abstract The CNRM‐Cerfacs Climate Prediction System (C3PS) is a new research modeling tool for performing climate reanalyzes and seasonal‐to‐multiannual predictions for a wide array of Earth system variables. C3PS is based on the CNRM‐ESM2‐1 model including interactive aerosols and stratospheric chemistry schemes as well as terrestrial and marine biogeochemistry enabling a comprehensive representation of the global carbon cycle. C3PS operates through a seamless coupled initialization for the atmosphere, land, ocean, sea ice and biogeochemistry components that allows a continuum of predictions across seasonal to multiannual time‐scales. C3PS has also contributed to the Decadal Climate Prediction Project (DCPP‐A) as part of the sixth Coupled Model Intercomparison Project (CMIP6). Here we describe the main characteristics of this novel Earth system‐based prediction platform, including the methodological steps for obtaining initial states to produce forecasts. We evaluate the entire C3PS initialization procedure with the most up‐to‐date observations and reanalyzes over 1960–2021, and we discuss the overall performance of the system in the light of the lessons learned from previous and actual prediction platforms. Regarding the forecast skill, C3PS exhibits comparable seasonal predictive skill to other systems. At the multiannual scale, C3PS shows significant predictive skill in surface temperature during the first 2 years after initialization in several regions of the world. C3PS also exhibits potential predictive skill in Net primary production (NPP) and carbon fluxes several years in advance. This expands the possibility of applications of forecasting systems, such as the possibility of performing multiannual predictions of marine ecosystems and carbon cycle.

The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature.