The ESA-SING dataset provides simulated satellite gravity products for three future gravimetry mission constellations: GRACE-C-like, NGGM, and MAGIC. The dataset is developed in the frame of the ESA SING project (<a href="https://www.esa-sing.org/" target="_blank" rel="noopener">www.esa-sing.org</a>) and is based on the ESA Earth System Model 2.0. For the first time, the ESA SING dataset delivers simulated gravity products (L2 & L3) consistently generated over a 12-year period, including a wide range of mass redistribution processes as well as mission-specific errors, arising from instrumental noise and background model errors. In addition to the simulated gravity fields and their residual errors, static empirical variance-covariance matrices for 5-day and 30-day solutions, derived from Monte Carlo simulations, enable the assessment of the expected mission uncertainties. The dataset enables users to assess the impact of future satellite gravimetry missions on applications in hydrology, oceanography, cryosphere, solid-Earth, climate, and geodesy across various temporal and spatial scales. A comparison with uncertainties from the historical GRACE record confirms that the uncertainties of the simulated GRACE-C-like mission are of the same order of magnitude, providing confidence in the validity of the ESA-SING dataset. A global evaluation of the products shows that the NGGM and MAGIC missions will enable high-frequency (sub-weekly) observations together with significantly improved spatial resolution for 30-day and long-term products. However, NGGM-only observations, while comparable to MAGIC in performance, are limited to latitudes between ±70° due to their inclined orbits. To address the polar gap, spherical cap regularisation is applied to NGGM-only solutions, and user recommendations are provided. Overall, the ESA-SING dataset allows users to evaluate mission-specific performance for upcoming satellite gravity missions. Its closed-loop simulations facilitate quantification of expected uncertainties in 5-day and 30-day gravity products, as well as trend and annual-cycle estimates. Moreover, the ESA SING dataset enables users to gain early experience in handling NGGM and MAGIC data and to prepare for the next generation of satellite gravimetry products.