The availability of precipitation data is the key driver in the application of hydrological models when simulating streamflow. Ground weather stations are regularly used to measure precipitation. However, spatial coverage is often limited in low-population areas and mountain areas. To overcome this limitation, gridded datasets from remote sensing have been widely used. This study evaluates four widely used global precipitation datasets (GPDs): The Tropical Rainfall Measuring Mission (TRMM)

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... the Climate Forecast System Reanalysis (CFSR), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), against point gauge and gridded dataset observations using multiple monthly water balance models (MWBMs) in four different meso-scale basins that cover the main climatic zones of Peninsular Spain. The volumes of precipitation obtained from the GPDs tend to be smaller than those from the gauged data. Results underscore the superiority of the national gridded dataset, although the TRMM provides satisfactory results in simulating streamflow, reaching similar Nash-Sutcliffe values, between 0.70 and 0.95, and an average total volume error of 12% when using the GR2M model. The performance of GPDs highly depends on the climate, so that the more humid the watershed is, the better results can be achieved. The procedures used can be applied in regions with similar case studies to more accurately assess the resources within a system in which there is scarcity of recorded data available. be available for a specific basin due to the malfunctioning of the equipment installed or the low density of stations [3] . Moreover, there can be important deviations between point-scale gauge information and true areal precipitation [4] [5] [6] [7] ; thus, the use of a grid dataset rather than a single rain gauge is advisable. In recent years, and to overcome the above limitations, global precipitation datasets (GPDs) have been widely used in the hydrology field. Besides being generally used as input data, GPDs are also employed for estimating input parameters for hydrological modelling [8] . Furthermore, reliable precipitation data are essential for hydrological modelling because their errors could lead to an inappropriate model setup, resulting in the wrong simulations and subsequent decisions [9] . Easy access, long-term series, and quality and homogeneity of data have encouraged the use of GPDs in hydrology [10] . These gridded datasets are very useful for hydrological modelling and provide potential alternative data sources for data-sparse and ungauged areas. The improvement of sensor technology has provided worldwide satellite observation data that are more spatially homogenous [1] . Some of the most commonly used products from satellite-derived data are the Tropical Rainfall Measuring Mission (TRMM) [11] and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) [12] . Moreover, it is becoming increasingly frequent to combine data from satellites with gauge measurements, resulting in more accurate tools in water balance models, for example, Multi-Source Weighted-Ensemble Precipitation (MSWEP) [13] . Beck et al. [14] validated MSWEP on a global scale using worldwide observations from more than 75,000 gauges, and gauge-corrected datasets were also evaluated using hydrological modelling for nearly 9000 catchments. The Climate Forecast System Reanalysis (CFSR) [15] is a third-generation reanalysis product [16] . It was designed and executed as a global, high-resolution, coupled atmosphere-ocean-land surface-sea ice system to provide the best estimate of the state of these coupled domains over the 1979-2014 period. The current CFSR will be extended as an operational, real-time product in the future. Gridded precipitation product errors may cause additional inconsistency in hydrologic simulations [17] , and, owing to the fact that GPDs are integrated systems, the uncertainty related to internal processing of observations (missing data, homogenization, atmospheric biases, etc.) can become difficult to evaluate [10] . Therefore, the study of hydrological outputs using various GPDs requires further investigation. Although some studies have been reported comparing global gridded precipitation datasets and their performance in driving hydrological models [18], most of them were carried out over large river basins. There is a need to improve our understanding of satellite precipitation products' performance over data-sparse and ungauged small watersheds [19] . To our knowledge, no studies have been carried out to investigate the efficiency of GPDs in driving hydrological models over Peninsular Spain. Furthermore, an appropriate hydrological model in a watershed is essential for providing accurate model predictions, and GPDs can be used for a better understanding of these processes [20, 21] , leading to improved model simulations. The development of monthly water balance models (MWBMs) is a complex task in a water resource system [22] . The appropriate analysis of their management is essential, especially in arid and semi-arid regions, where precipitation is very unevenly distributed with high evapotranspiration (ETP) rates. The spatial structure of a MWBM can be divided into three categories: Lumped, semi-distributed, and fully distributed [23] . In a lumped water balance model, catchment parameters and variables are averaged in space, so hydrological processes are approached through conceptual solutions formulated by using semi-empirical equations, while semi-distributed and fully distributed models process spatial variability by homogeneous zones or grid cells, respectively. However, it is not only spatial discretization that determines the quality of the simulation. The choice of model is dictated by the modelling purpose. When flow at the catchment outlet is the main required goal in water resource management, as in the present paper, lumped models may be the best choice [24] . Currently, the ABCD model was found to have satisfactory results in Greece [25] . Wriedt and Bouraoui [26] used GR2M in nearly 500 catchments in Germany, France, Spain, and Portugal and obtained good results both in the centre and north of Spain, and in Central European basins. The Australian water balance model (AWBM) is one of the most widely used rainfall/run-off