Many numerical studies have shown a significant sensitivity of near-surface climatological variables to soil moisture levels [e.g., Shukla and Mintz, 1982; Rind, 1982; Yeh et al., 1984; Sud and Fennessy, 1984; Fennessy and Shukla, 1999; Koster et al., 2000; Hong and Kalnay, 2000]. More recent numerical studies have shown that, in boreal summer midlatitudes, soil moisture can play a role as importantas that of the sea surface temperature in controlling the continental precipitation variability [Kumar and Hoerling, 1995; Trenberth and Branstator, 1992; Shukla, 1998; Koster and Suarez, 2000; Koster et al., 2004]. All above sensitivity studies have demonstrated that accurate soil moisture initial conditions can potentially improve subseasonal forecasts of near surface variables, particularly during boreal summers. However, most of these studies use extreme values of soil moisture initial conditions (for instance, almost dry or almost saturated). The literature dealing with the use of realistic soil moisture initial conditions is not very extended. Progress in addressing this question has been hampered by the lack of reliable global soil moisture observations to initialize global climate models. Indeed, the heterogeneity of the soil properties (e.g. porosity, permeability), the topography and the land cover types, make a global soil moisture measurement difficult. Today, this variable is sparsely measured in-situ and is not well estimated by satellite remote sensing. Despite their significant advances, the current remote sensing techniques for soil moisture still suffer from issues associated with the shallow depth of the retrieval (less than 5 cm), the absence of retrieval over dense vegetated and frozen areas, and significant uncertainties in the retrieval algorithm. To fill this gap, soil moisture analysis techniques are often used. In an ongoing model intercomparison project named the 2nd phase of the Global Land Atmosphere Coupling Experiment (GLACE-2) [Koster et al., 2011], almost all participants drive their Land Surface Model (LSM) in an offline mode using the GSWP-2 observation-based atmospheric forcing data. The offline land surface state variables are then used to initialize the coupled land-atmosphere model. However, because the offline simulation and the coupled land-atmosphere model have most likely different climatologies, the near surface atmospheric state of the forecasts may undergo a bias adjustment (or spinup). This spinup problem can decrease the short-term to subseasonal forecast skill. To solve this problem, a climatological correction is applied in GLACE-2 to the offline simulations before initializing the forecasts [van den Hurk et al., 2012]. Another alternative to solve the spinup problem is to use a coupled land-atmosphere model for both the initialization and the forecast. In this latter alternative, no climatological correction is required. Starting in 2002, reanalysis products provided by operational centers were among the first to use a land assimilation system using a land-atmosphere model in a coupled mode. For instance, the National Center for Environmental Prediction (NCEP)/Department of Energy (DOE) Reanalysis 2 (R2) adjusts the top 10 cm soil moisture using the difference between the modeled and the 5-day mean of CPC Merged Analysis of Precipitation (CMAP) precipitation rates [Kanamitsu et al., 2003]. However, when the atmospheric physics of the model simulates an error such as a clear sky while a heavy observed rain event is assimilated into the land surface that will, in turn, produce a wet soil moisture analysis. The resulting strong radiative and surface flux adjustments can impair the quality of the soil moisture analysis. This physical inconsistency between the soil moisture analysis and the atmospheric physics of the model reduces the soil moisture predictability. Current efforts have also been put into the NCEP Coupled Forecast System Reanalysis (CFSR) to produce a soil moisture analysis. CFSR performs uncoupled integration of its land surface model driven by the CMAP precipitation data every 24-hours. The offline soil moisture and soil temperature estimates are then used as initial conditions of the CFSR for the following 24-hours. Since it is a similar offline land assimilation approach as that used by GLACE-2, a spinup problem can be encountered (explained above). The aim of the part I of this paper is to produce a new soil moisture analysis using (1) a physically consistent land assimilation system and (2) a land-atmosphere model in coupled mode. In contrast with the offline soil moisture initialization technique used in GLACE-2, a coupled land-atmosphere model is used, and thus no climatological correction is required. The land assimilation system used in this study is called Precipitation Assimilation Reanalysis (PAR) and consists of assimilating high frequency (3-hourly) observation-based precipitation data into the atmospheric component of the model in a continuous assimilation period of a few months. Since precipitation is a diagnostic variable, the precipitation assimilation is performed by adjusting the vertical profile of the atmospheric humidity based on the difference between the model and the observed precipitation rates. A Newtonian nudging of dynamical variables (surface pressure, vorticity, divergence, temperature) toward R2 also is applied to reduce any model drift from the observed large scale atmospheric circulation. The combination of the dynamical nudging and the adjustment of the atmospheric humidity vertical profile not only brings the model precipitation estimates close to the observations but also redistributes the atmospheric heat and moisture, which in turn affects the adiabatic heating and hence the cloudiness. Therefore, unlike in R2, the radiative fluxes (directly affected by the cloudiness) and the surface fluxes remain physically consistent with the soil moisture analysis. This paper is organized as follows. Section 2 describes the PAR technique and the precipitation data sets used to apply and validate the PAR technique. Section 3 introduces the different soil moisture data sets used for the soil moisture analysis evaluation. Section 4 presents the experimental design. Finally, the results and conclusion are provided in sections 5 and 6 respectively.