## Data and Methods
### Evaluation Datasets and Methods  
The LDAS-Monde analysis impact was assessed with respect to the open-loop model run (i.e., no assimilation). The system was spun-up by running year 2010 twenty times. Table 1 presents the set up of the different experiments used in this study, the open-loop and the analysis, as well as two additional model runs: (i) Ini_model, a 12-month model run starting on 1 January 2016 (initialised by the model, that is, the openloop with no data assimilation, simulation run from 2010 to 2015); and (ii) Ini_analysis, a 12-month model run initialised by initial conditions from the analysis on 1 January 2016. The two above-mentioned assimilated datasets (ESA CCI SSM and LAI GEOV1) were used as a way to check to what extent the assimilation system was able to produce analyses closer to these two datasets that were assimilated than the open-loop. Then, two independent spatially distributed datasets, namely evapotranspiration from the GLEAM project [45; 46] and gross primary production (GPP) from the FLUXCOM project [47; 48], were used in the evaluation process. Ground based measurements of soil moisture from the USCRN (US Climate Reference Network, [49]) were also used, along with river discharge observations from the United States Geophysical Survey (USGS) and the Global Runoff Data Centre (GRDC). The ability of LDAS-Monde to represent SSM, LAI, evapotranspiration, and GPP was assessed using the correlation coefficient (R) and root mean square difference (RMSD). These metrics were applied at a seasonal scale (i.e., for each month) over 2010-2016. For ground-based measurements of SSM, R was calculated for both absolute and anomaly time series in order to remove the strong impact from the SSM seasonal cycle on this specific metric (see e.g., [13; 28]). Ground measurements at a depth of 5 cm were compared with soil moisture of the third layer of soil (between 4 and 10 cm depth) from both the model and the analysis for months from April to September over the 2010-2016 time period to avoid frozen conditions. Only stations with significant R values for the two experiments (with \(p\)-value \(<\) 0.05) were kept for the evaluation. In order to provide an easier measurement of the added value of the analysis, statistics were also normalized with respect to the model. The so-called normalized information contribution index (NIC as in [28; 50]) was applied to the correlation coefficient (Equation (1), for both volumetric and anomaly time-series) and to RMSD (Equation (2)) to quantify the improvement or degradation from the analysis with respect to the model. \[\text{NIC}_{\text{R}}=\frac{\text{R}_{\text{(Analysis)}}-\text{R}_{\text{( Model)}}}{1-\text{R}_{\text{(Model)}}}\times 100 \tag{1}\]  
\[N_{\text{RMSD}}=\frac{\text{RMSD}_{\text{(Analyse)}}-\text{RMSD}_{\text{( Model)}}}{\text{RMSD}_{\text{(Model)}}}\times 100 \tag{2}\]  
NIC scores were then classified according to three categories: (i) negative impact from the analysis with respect to the model with values smaller than \(-\)3%, (ii) positive impact from the analysis with respect to the model with values greater than \(+\)3%, and (iii) neutral impact from the analysis with respect to the model with values between \(-\)3% and 3%. Over the 2010-2016 time period, river discharge from the analysis and model runs were compared with daily streamflow data from USGS and GRDC. Data were selected for sub-basins with rather large drainage areas (10,000 km\({}^{2}\) or greater) because of the low resolution of CTRIP (0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\)) and with a long observation time series (48 months or more). As commonly found in the literature, observed and simulated river discharge (Q) data are expressed in m\({}^{3}\) s\({}^{-1}\). However, given that the observed drainage areas may differ from the simulated ones, specific discharge in mm d\({}^{-1}\) (the ratio of Q to the drainage area) was used in this study, similarly to [13; 28]. Stations with drainage areas differing by more than 20% from the simulated ones were discarded.