The volume of Earth system observations has grown massively in recent decades. However, multivariate or multisource analyses at the interface of atmosphere and land are still hampered by the sparsity of ground measurements and the abundance of missing values in satellite observations. This can hinder robust multivariate analysis and introduce biases in trends. Nevertheless, gap-filling is often done univariately, which can obscure physical dependencies. Here, we apply the new multivariate gap-filling framework CLIMate data gapFILL (CLIMFILL). CLIMFILL combines state-of-the-art spatial interpolation with an iterative approach accounting for dependencies across multiple incomplete variables. CLIMFILL is applied to a set of remotely sensed and in situ observations over land that are central to observing land-atmosphere interactions and extreme events. The resulting gridded monthly time series covers 1995-2020 globally with gap-free maps of nine variables: surface layer soil moisture from European Space Agency (ESA)-Climate Change Initiative (CCI), land surface temperature and diurnal temperature range from Moderate-resolution Imaging Spectroradiometer, precipitation from GPM, terrestrial water storage from GRACE, ESA-CCI burned area, and snow cover fraction as well as 2-m temperature and precipitation from CRU. Time series of anomalies are reconstructed better compared to state-of-the-art interpolation. The gap-filled data set shows high correlations with ERA5-Land, and soil moisture estimates compare favorably to in situ observations from the International Soil Moisture Network. Soil moisture drying trends in ESA-CCI only agree with the reanalysis product ERA5-Land trends after gap-filling. We furthermore showcase that key features of droughts and heatwaves in major fire seasons are well represented. The data set can serve as a step toward the fusion of multivariate multisource observations.
