The datasets provided in support of the study 'Unprecedented Continental Drying,Shrinking Freshwater Availability, and Increasing Land Contributions to SeaLevel Rise' by Chandanpurkar et al, 2025. Questions regarding these data can be addressed to [email protected] datasets are as follows:1. gic_mask.nc: The mask used to identify JPL mascons that feature signals from mountain glaciers and ice caps. 2. four_megaregions.shp: The drying mega-regions identified in the study. 3. high_res_local_trends.nc: The local trends from enhanced-resolution,bias-corrected TWS data for the period 2/2003-4/2024. The units are km3 yr-1. 4. persistant_local_trends.nc: The results from the sensitivity analysis forpersistence of the local trends to an incrementally increasing duration of GRACErecords, shown in Figure 4a. The units are in percentages. The locations with 0%value have always shown positive trends, and the 100% value represents locationsthat have always shown negative trends to the increasing GRACE record length.The persistent wetting trends are considered for values between 0-5%, whilepersistent drying trends are for values between 95-100%. 5. ratio_t_ia.nc: The map of local ratios of trend variance to interannual variance,shown in Figure 4b. Values greater than 1 indicate that the long-term trendvariance exceeds interannual variability.

The datasets provided in support of the study 'Unprecedented Continental Drying,Shrinking Freshwater Availability, and Increasing Land Contributions to SeaLevel Rise' by Chandanpurkar et al, 2025. Questions regarding these data can be addressed to [email protected] datasets are as follows:1. gic_mask.nc: The mask used to identify JPL mascons that feature signals from mountain glaciers and ice caps. 2. four_megaregions.shp: The drying mega-regions identified in the study. 3. high_res_local_trends.nc: The local trends from enhanced-resolution,bias-corrected TWS data for the period 2/2003-4/2024. The units are km3 yr-1. 4. persistant_local_trends.nc: The results from the sensitivity analysis forpersistence of the local trends to an incrementally increasing duration of GRACErecords, shown in Figure 4a. The units are in percentages. The locations with 0%value have always shown positive trends, and the 100% value represents locationsthat have always shown negative trends to the increasing GRACE record length.The persistent wetting trends are considered for values between 0-5%, whilepersistent drying trends are for values between 95-100%. 5. ratio_t_ia.nc: The map of local ratios of trend variance to interannual variance,shown in Figure 4b. Values greater than 1 indicate that the long-term trendvariance exceeds interannual variability.

These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. The ocean mass balance is obtained from these components: dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. E-P: Here, two methods are used: 1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P 2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products. The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename.

These files include a time series of global continental discharge estimated from ocean mass balance following Chandanpurkar et al., 2017. The ocean mass balance is obtained from these components: dM/dt (change in ocean mass): From altimetry and from GRACE/FO. When derived from altimetry, steric level change is subtracted from the GMSL using EN4.2.2 temperature and salinity data. E-P: Here, two methods are used: 1. Directly, using estimates of ocean E and P, using OAFlux for E and GPCP and CMAP separately for P 2. Indirectly, using atmospheric moisture balance using vertically integrated horizontal moisture flux divergence, and change in the total column water vapor. These are obtained using ERA5 and MERRA-2 reanalyses products. The eight discharge estimates are combinations of the above, and the exact combination is mentioned in the filename.

We calculate an ensemble of global land evapotranspiration (ET) for 2003 to 2019 over global land using a water-budget approach. We use 4 publicly available precipitation datasets (GPCPv2.3, MERRA-2, ERA-5 and NOAA-NCEP), 5 discharge estimates (JRA-55, and 4 independently calculated ocean -mass balance global discharge estimates) and water storage change derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) missions. We calculate 20 different estimates of global land evapotranspiration using all combinations of the precipitation and discharge datasets, and one estimate of total water storage change (computed using backward difference method and the GRACE/GRACE-FO total water storage change from JPLRL06). The data is presented as a timeseries from 2003 to 2019 at a monthly time step (in units of mm per year). We also provide an estimate of monthly uncertainty based on error in the precipitation data sets (defined as the standard deviation across the precipitation data), error in discharge (defined as standard deviation across the discharge data) and error in the water storage change (this is calculated using the GRACE formal error product) as well as the total error (from summing in quadrature the mean component errors) ('Global-land-ET-error-budget'). The primary dataset is an estimate for all land areas including the ice-sheets ('Global-land-ET'). We also provide two separate estimates of global land ET that: i) do not include the contribution of the ice sheets (Greenland and Antarctica) ('Global-land-ET-without-icesheets'), ii) do not include the contribution of Antarctica ('Global-land-ET-without-Antarctica'). For each ensemble member of ET, the data variable contains the name of the precipitation data set and discharge data set used. We also include the data that has been smoothed and gap filled using bootstrapping methods ('Global-land-ET-smoothing-bootstrap'). All data is monthly and in units of mm/year. We also include the global discharge ocean mass balance estimates that were used to estimate global land evapotranspiration. The data set of global discharge is available for 2002 to 2019 using an ocean mass balance approach. The data was created using ocean altimetry (AVISO/DUACS), ocean steric information (EN4), combined with ocean precipitation (GPCPv2.2, CMAP), ocean evaporation (OAFLUX), and also estimates of precipitation - evaporation calculated from ocean atmospheric moisture budget (MERRA-2, ERA-5). The discharge includes runoff from all land masses including the ice sheets. The data was created by H. Chandanpurkar, and is an updated version from Chandanpurkar et al. (2017). Details are available at: Chandanpurkar, H. A., Reager, J. T., Famiglietti, J. S., & Syed, T. H. (2017). Satellite-and reanalysis-based mass balance estimates of global continental discharge (1993–2015). Journal of Climate, 30(21), 8481-8495.

We calculate an ensemble of global land evapotranspiration (ET) for 2003 to 2019 over global land using a water-budget approach. We use 4 publicly available precipitation datasets (GPCPv2.3, MERRA-2, ERA-5 and NOAA-NCEP), 5 discharge estimates (JRA-55, and 4 independently calculated ocean -mass balance global discharge estimates) and water storage change derived from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) missions. We calculate 20 different estimates of global land evapotranspiration using all combinations of the precipitation and discharge datasets, and one estimate of total water storage change (computed using backward difference method and the GRACE/GRACE-FO total water storage change from JPLRL06). The data is presented as a timeseries from 2003 to 2019 at a monthly time step (in units of mm per year). We also provide an estimate of monthly uncertainty based on error in the precipitation data sets (defined as the standard deviation across the precipitation data), error in discharge (defined as standard deviation across the discharge data) and error in the water storage change (this is calculated using the GRACE formal error product) as well as the total error (from summing in quadrature the mean component errors) ('Global-land-ET-error-budget'). The primary dataset is an estimate for all land areas including the ice-sheets ('Global-land-ET'). We also provide two separate estimates of global land ET that: i) do not include the contribution of the ice sheets (Greenland and Antarctica) ('Global-land-ET-without-icesheets'), ii) do not include the contribution of Antarctica ('Global-land-ET-without-Antarctica'). For each ensemble member of ET, the data variable contains the name of the precipitation data set and discharge data set used. We also include the data that has been smoothed and gap filled using bootstrapping methods ('Global-land-ET-smoothing-bootstrap'). All data is monthly and in units of mm/year. We also include the global discharge ocean mass balance estimates that were used to estimate global land evapotranspiration. The data set of global discharge is available for 2002 to 2019 using an ocean mass balance approach. The data was created using ocean altimetry (AVISO/DUACS), ocean steric information (EN4), combined with ocean precipitation (GPCPv2.2, CMAP), ocean evaporation (OAFLUX), and also estimates of precipitation - evaporation calculated from ocean atmospheric moisture budget (MERRA-2, ERA-5). The discharge includes runoff from all land masses including the ice sheets. The data was created by H. Chandanpurkar, and is an updated version from Chandanpurkar et al. (2017). Details are available at: Chandanpurkar, H. A., Reager, J. T., Famiglietti, J. S., & Syed, T. H. (2017). Satellite-and reanalysis-based mass balance estimates of global continental discharge (1993–2015). Journal of Climate, 30(21), 8481-8495.