We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies (TWSA) over the global land except for Greenland and Antarctica with a spatial resolution of 0.5\(^\circ \), covering the time frame 2003 to 2019 without gaps, and including monthly uncertainty quantification. GLWS2.0 was derived by assimilating monthly GRACE/-FO mass change maps into the WaterGAP global hydrology model via the ensemble Kalman filter, taking data and model uncertainty into account. TWSA in GLWS2.0 is then accumulated over several hydrological storage variables. In this article, we describe the methods and data sets that went into GLWS2.0, how it compares to GRACE/-FO data in terms of representing TWSA trends, seasonal signals, and extremes, as well as its validation via comparing to GNSS-derived vertical loading and its comparison with a version of the NASA Catchment Land Surface Model GRACE Data Assimilation (CLSM-DA). We find that, in the average over more than 1000 stations globally, GLWS2.0 correlates better with GNSS observations of vertical loading at short-term, seasonal, and long-term temporal bands than GRACE/-FO. While some differences exist, overall GLWS2.0 agrees reasonably well with CLSM-DA in terms of TWSA trends and annual amplitudes and phases.Highlights

• We describe the new global land water storage data set GLWS2.0, which contains total water storage anomalies over the global land with a spatial resolution of 0.5\(^\circ \), covering the period 2003 to 2019 without gaps, and including uncertainty quantification.

• GLWS2.0 synthesizes monthly GRACE/-FO mass change maps with daily precipitation and radiation data via the WaterGAP model framework, taking data and model uncertainty into account.

• Here we describe the methods and data sets that went into GLWS2.0 and its validation from a geodetic applications perspective. We find that, in the global average, GLWS2.0 fits better than GRACE/-FO to GNSS observations of vertical loading.