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Gridded precipitation datasets have been developed for data assimilation and evaluation tasks of weather and climate models and for climate analyses. Gridded data uncertainty evaluation is crucial to understand the limitations and feasibility. The development of high‐resolution daily gridded precipitation datasets is desirable, but several factors need to be considered, namely rain gauge station availability, their spatial distribution, and orographic and climate characteristics of a study area. Quality assessment of gridded datasets can present difficulties when the influence of these factors is not thoroughly analysed. The main objective of this study was a detailed validation of precipitation grids based on four factors, that is, station density used for grid construction, grid spatial resolution, station altitude, and climate type. To this end, 18 grids were built using six spatial resolutions (0.01°, 0.025°, 0.05°, 0.1°, 0.2° and 0.4°) and three station densities (25, 50 and 75% of all available stations). Results indicate larger differences among the grids as a function of analysed factors. Station density was found to be the main factor, whereas grid spatial resolution had minor importance. However, the latter factor becomes more relevant in areas with strong altitude gradients and when a high station density is available. In addition, weak and moderate precipitation is overestimated on daily grids, whereas heavy precipitation cells are less frequent, reducing data variability. On the contrary, monthly and annual aggregates present less deviation from the observed distribution than daily comparisons. These findings question the applicability of the daily grid datasets for validation studies and climate analysis on a grid cell level. [ABSTRACT FROM AUTHOR]

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation. [ABSTRACT FROM AUTHOR]