Daily precipitation is a key variable to understanding the behaviour of extreme weather events and the severe impacts they cause on hydrological systems, in natural systems and on human societies. These impacts can be considered in regional and local plans which can help to mitigate major disasters if the correct environmental data are available. Unfortunately, the raw climatic information is usually highly fragmented in time and discontinuous in space. Quality control and reconstruction processes are therefore in high demand, as well as final products such as serially continuous observational series or gridded datasets. High-resolution spatiotemporal precipitation datasets are useful tools for land management, and their availability over a complete region can be useful to many other fields due to the relevance of precipitation in many disciplines as hydrology (for instance in water resources management at catchment scale) (e.g. Werner and Cannon, 2016; Kay et al., 2015; Lorenz et al., 2014; Maurer et al., 2002), environmental risks such as droughts (e.g. Touchan et al., 2011; Vicente-Serrano et al., 2010a; Bordi et al., 2009; Andreadis et al., 2005), floods (e.g. Hartmann and Andresky, 2013; Rojas et al., 2011) or wildfires (Westerling et al., 2003), groundwater recharge (e.g. Döll and Fiedler, 2008) or agricultural applications (e.g. Mishra and Cherkauer, 2010; Lo et al., 2007). Upon the development of these kinds of datasets, the researchers cannot only improve their studies in analytic climatology but also in environmental studies, habitat modelling, climate change model validations, relations of climate and vector diseases, crop forecasting, agroclimatic selection of plant landraces, palaeoclimatic analysis supported on the observational period, etc. Gridded datasets provide valuable products that can be used for both scientific and decision-making policy purposes.

The number and density of climate observatories are finite. Therefore, there have been many contributions over the last decade to solve this problem by creating high-resolution and world-based gridded datasets based on the (geo)statistical relations of the observatories (New et al., 2002; Hijmans et al., 2005; Di Luzio et al., 2008; Harris et al., 2014; Schamm et al., 2014). However, these global datasets are often not optimum for regional analysis, where a higher resolution both in space and time is required (Herrera et al., 2012). Increasing temporal resolution from monthly or annual to daily scale allows analysis of other relevant components of the climate, such as the extreme precipitation events or the length and intensity of dry spells that precede most of the annual hydrological processes. In this respect, daily precipitation grids of different spatial resolutions have been developed at the global (Piper and Stewart, 1996; Menne et al., 2012) or regional scale (Frei and Schär, 1998; Eischeid et al., 2000; Kyriakidis et al., 2001; Rubel and Hantel, 2001; Klein-Tank et al., 2002; Hewitson and Crane, 2005; Liebmann and Allured, 2005; Haylock et al., 2008; Klok and Klein-Tank, 2009; Mekis and Vincent, 2011; Hwang et al., 2012; Yatagai et al., 2012; Jones et al., 2013; Chaney et al., 2014; Isotta et al., 2014; Hernández et al., 2016).

A few daily datasets have also been made for Spain or some of its regions (Vicente-Serrano et al., 2010b; Herrera et al., 2012). These datasets are useful to analyse global precipitation, which in Spain have a wide range of different spatial and temporal distributions (Esteban-Parra et al., 1998; Rodríguez-Puebla et al., 1998; Belo-Pereira et al., 2011; De Luis et al., 2011; González-Hidalgo et al., 2011; Cortesi et al., 2014). But they are also suitable to analyse spatial patterns in daily characteristics of precipitation through extreme indices that allow comparisons between regions. In Spain, these indices have been applied to mainland Spain (Martín-Vide, 2004; Herrera et al., 2012; Merino et al., 2015) and to specific regions (Casas et al., 2007; Martínez et al., 2007; Rodrigo and Trigo, 2007; Lopez-Moreno et al., 2010). All of these datasets used the precipitation estimations to compute climatic indices. However, unlike temperature, uncertainty in precipitation estimation is considerably higher. Firstly, rain-gauge measurements can be notoriously uncertain, especially in certain circumstances such as windy conditions (Rodda and Dixon, 2012) that can lead to significant undercatches in highly exposed areas (Rodda and Smith, 1986). Secondly, spatial variability of daily precipitation can be extremely high under certain atmospheric conditions such as convective processes that can occur at a very local scale. Whereas the sum of these uncertainties is very difficult to estimate in practice, the uncertainties of these estimations are expected to decrease as the density of observations used to compute the models increases (Tveito et al., 2008; Hofstra et al., 2010). This issue has important implications in subsequent climatic analyses, and this is one consideration that needs to be taken into account (Beguería et al., 2015).

In this paper, we present SPREAD (Spanish PREcipitation At Daily scale), a new high-resolution daily gridded precipitation dataset for Spain. Thirteen daily and extreme precipitation indices were calculated as an example of applicability, characterising daily precipitation distribution (4 indices to characterise the spatial distribution of daily precipitation and 9 for extreme precipitation). The grid was computed for 1950–2012 period covering peninsular Spain and 1971–2012 for Balearic and Canary islands. It was built from 12 858 original stations by creating reference values (RVs) using generalised linear models (GLMs) based on the 10 nearest observations using altitude, longitude and latitude as covariates. All the calculations were developed with reddPrec (https://cran.r-project.org/web/packages/reddPrec), an R package containing the required functions to reconstruct original daily precipitation series and create grids (Serrano-Notivoli et al., 2017a).

This paper is organised as follows. Section 2 describes the original dataset. Section 3 shows the methods applied for the reconstruction and the gridding process considering uncertainties of estimates. Also, the statistical procedures are explained here. Section 4 shows the results of the reconstruction, gridding procedure and spatial distribution of daily and extreme precipitation characteristics in Spain. Section 5 specifies the availability of the dataset. The results are discussed in Sect. 6, with some final remarks in Sect. 7.

A total of 12 858 observatories covering Spain were used. The grid was divided in three areas: (1) peninsular Spain (492 175 km2), with 11 513 stations covering the period 1950–2012; (2) the Balearic Islands (4992 km2), with 425 stations covering 1971–2012; and (3) the Canary Islands (7493 km2) covering 1971–2012, using 920 stations (Fig. 1 down).

Most of the data were provided by the Spanish Meteorological Agency (AEMET), but we also used data from regional hydrological and meteorological services, and from the national agronomic network (Table 1). The greatest part of the information comes from manual stations; the automated ones entered service in mid-1990s, making up 23 % of the total in AEMET network in 2012. The mean length of the original series was 18.8 years, and only 17 of the 12 858 original observatories had covered the period 1950–2012. However, the spatial distribution of the observatories showed a remarkable density (Fig. 1 main map), which is useful to make proper reconstructions. Although the recovered information from raw databases of the meteorological offices was precipitation, in some cases this can include both rainfall and the water equivalent of snow if the source did not make the distinction.

High-resolution gridded datasets are useful for regional analysis of daily precipitation, but the accuracy of the estimates depends mainly on the number of available observatories and on the estimation method. Although the method used to build this grid makes independent calculations of each grid point and day, the results showed coherent patterns in spatial distributions of all indices at regional scales.

Some basic parameters of the reconstruction methodology have a key influence on the dissimilarity between different datasets. The selection of one specific method from the many available gridding interpolation methods that can be applied to precipitation may change the final result (e.g. Creutin and Obled, 1982; Hartkamp et al., 1999; Vicente-Serrano et al., 2003; Dobesch et al., 2007; Hofstra et al., 2008; Hwang et al., 2012; Brunetti et al., 2014; Militino et al., 2015; Contractor et al., 2015; Herrera et al., 2016). For example, Robeson and Ensor (2006) and Ensor and Robeson (2008) argued that the use of geostatistical interpolators for daily precipitation leads to a higher frequency of low-precipitation values while greatly reducing the extreme events. In addition, the high flexibility in the independent variables across the sites allows for a reasonable estimation of the uncertainty, which is very important for producing datasets that will feed further analyses. Local regressions have been used widely to model daily precipitation with different approaches (Buishand and Tank, 1996; Rajagopalan and Lall, 1998; Marquínez et al., 2003; Simolo et al., 2010; Tardivo and Verti, 2014; Partal et al., 2015), and in this case we used them to compute, from all reconstructed stations, a high-resolution grid estimating separately the probability of a wet/dry day occurrence and the precipitation amount. This two-step procedure avoids an excessive smoothness of the estimated precipitation fields (Robeson and Ensor, 2006). Furthermore, this individualised calculation allows for an easy update of the dataset, since single days can be reconstructed individually and added to the pre-existing dataset. We also added a measure of uncertainty, which is a big improvement over previous gridded datasets. Uncertainty (which we express by means of the standard error) informs in a quantitative way about the reliability of the estimated data, in a way that can be translated to further calculations such as the daily precipitation indices explored in this article. Our uncertainty estimation arises from a local interpolation, so it varies spatially and from one day to the next, reflecting the changes in conditions that affect the estimates.

Although some previous datasets exist that obtain daily and/or extreme precipitation indices for Spain, the different methodologies to compute them are a key influence in these differences. The use of the longest precipitation series and the spatial resolution of the final grid produces very different results. For instance, considering the global datasets, Sillmann et al. (2013) showed for Spain (eight grid points covering the whole of peninsular Spain) RX5 values from 50 and 200 mm using the HadEX2 dataset and from 40 and 75 mm using CMIP5 dataset. In the present work, these values ranged between 50 and more than 300 in more than 20 000 grid points. These values were the maximums in HadEX2 and CMIP5 for monsoon areas in southern Asia. Similarly, May (2007), using the HIRHAM model, showed SDII values for Spain between 4.5 and 12.5, while in the present work we found that this index can reach values over 25 mm, especially in the Central Range. Schamm et al. (2014) used the GPCC (Global Precipitation Climatology Centre) dataset to show values of daily mean precipitation intensity (PMED) between 0.5 and less than 10 mm, while we found a wider range between 3 and more than 15 mm.

Some previous works in Spain that used a lower spatial resolution and a lower number of observatories (Herrera et al., 2012; Merino et al., 2015) showed overestimated values in extreme precipitation indices in some areas (especially in the north-west area of the Iberian Peninsula) but smoothing in others (e.g. Central Range). López-Moreno et al. (2010) showed similar values and spatial patterns in north-east Spain, compared to the ones in the present work for NWD, SDII, CDD and CWD. Despite the relatively high-density station dataset (217 stations) used in their study, they rejected most of the original stations in favour of only the longest series, resulting in a smoothed spatial distribution of the indices, probably also due to the interpolation method (not indicated). Martínez et al. (2007), using 75 stations, showed for Catalonia (northeast peninsular Spain) the 95th percentile of precipitation values ranging from 20 to 70 mm, which are very similar to those from the SPREAD dataset. All of these works made sub-optimal use of the available data. The values obtained in all of them were correct considering a global conception of precipitation distribution, but daily precipitation requires the highest possible density of observations in order to obtain a proper characterisation of its spatial distribution, especially for extreme precipitation. This work provides a representation of the local variability of extremes by using all the available information and applying a local reconstruction method. If the spatial resolution is amplified for a regional study, based on the use of more precipitation data, the results are less smoothed as shown in Pereira et al. (2016), which used 36 stations in Sierra Nevada (southern peninsular Spain) to compute NWD, R10mm and R20mm, with similar values to this work specifying a precise spatial distribution. The use of the complete information of the precipitation network in Spain provided a more detailed precipitation distribution over time and space. Although only a few stations covered the complete period, the use of short data series helped to estimate the missing precipitation values in longer ones, which were used to build the whole grid. A high number of grid points (higher spatial resolution) in combination with a low-density stations network could lead to higher uncertainties. This work aimed to set a compromise between both factors by using a high number of stations and a medium-high spatial resolution. In addition, the magnitude of the uncertainty informed about the reliability of each estimate. A higher uncertainty means more differences between the data used to estimate precipitation and these differences can be increased with a lower number of stations.