Internal climate variability encompasses processes ranging from daily weather fluctuations to multidecadal interactions within the climate system. Understanding these processes is crucial for distinguishing natural variability from human-induced climate change. A large component of internal variability on sub-seasonal to multi-decadal time scales are associated with recurring patterns or ‘climate modes’. Using pre-industrial control (piControl) simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we investigate eight critical climate modes: Eastern Pacific El Niño (EP-El Niño), Central Pacific El Niño (CP-El Niño), Interdecadal Pacific Oscillation (IPO), Indian Ocean Dipole (IOD), Subsurface Dipole Mode (SDM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), and Southern Annular Mode (SAM). These modes were derived from 23 CMIP6 models, each with over 500 years of simulation data, ensuring robust statistical insights into their spatial and temporal structures. The datasets were validated against observational data, revealing broad-scale consistency and highlighting biases in regional features and amplitudes. For example, the models effectively capture spatial patterns such as the tripolar SST anomaly of the IPO and the equatorial Pacific warming of EP-El Niño. However, regional discrepancies, like exaggerated warming or cooling in specific areas, were observed. Despite these biases, the datasets provide critical tools for understanding climate variability, conducting detection and attribution studies, and improving climate projections. Details regarding the generated NetCDF files are provided in the accompanying README file. All datasets are publicly accessible (https://doi.org/10.5281/zenodo.17274477, and additionally linked to this record), supporting future research and policy development to address climate variability and its implications for climate change adaptation and mitigation.