Almost all previous inversions for CO2 sources and sinks from atmospheric measurements have used monthly or annual mean data. Here, we assess the potential benefits and challenges of using higher time resolution CO2 concentration data. Inversions are performed with synthetic data since there are currently only limited continuous CO2 measurements available. The use of synthetic data also enables the inversion quality to be evaluated in terms of both systematic biases in the estimated sources and the magnitude of the source uncertainties. When estimating sources for 22 large regions, we find that using high time resolution data gives large reductions in uncertainty compared to using monthly mean data. Greater reductions are achieved (around 80%) for smaller rather than larger networks (around 45%), although larger networks give lower uncertainties overall (0.35 Gt C yr−1 on average compared to 1.2 Gt C yr−1). In most cases, inversion biases are larger than the estimated uncertainties (by up to 6 Gt C yr−1) due to the assumptions made in the inversion about the spatial distribution of sources within regions. These biases can be significantly reduced by estimating the sources for smaller regions. This is demonstrated using a case study that subdivides Australia into the model grid cells. Sources are successfully estimated from high time resolution data for the individual grid cells and for the total Australian source.