## 1. Introduction

[2] Gridded climate data derived from meteorological station measurements underpin a wide range of applications and research in climate science, including evaluation of global and regional climate models, the construction of bias-corrected climate change scenarios and driving many applications in climate impacts assessments [*Haylock et al.*, 2008]. Increasingly, there has been a need for gridded data at higher spatial and temporal resolutions, as the focus of climate change research has shifted from global to regional and local scales. Recently, *Haylock et al.* [2008] described the development of the first high-resolution gridded data set of daily climate over Europe (termed E-OBS), as part of the EU funded ENSEMBLES project. The data set, comprising daily mean, minimum and maximum temperature and precipitation, was constructed through interpolation of the most complete collection of station data over wider Europe [*Klok and Klein Tank*, 2008]. The data are available on four different Regional Climate Model (RCM) grids (0.25 and 0.5 degree regular latitude-longitude and 0.22 and 0.44 degree rotated pole) and cover the period 1950–2006. Additionally, estimates of interpolation uncertainties are included as part of the data set [*Haylock et al.*, 2008].

[3] Gridded data sets derived through interpolation of station data have a number of potential inaccuracies and errors. Errors in the underlying station data can be propagated into the gridded data; typical sources of error include incorrect station location information and individual erroneous values or nonclimatic breaks (inhomogeneities) in the station time series. A second source of uncertainty relates to the ability of the interpolation method to estimate grid values from the underlying station network. In general, interpolation accuracy decreases as the network density decreases, is less accurate for variables with more variable spatial characteristics (e.g., precipitation) and degrades in areas of complex terrain (e.g., mountain areas). While E-OBS is based on the largest available pan-European data set and the interpolation methods used were chosen after careful evaluation of a number of alternatives [*Hofstra et al.*, 2008], the data set will inevitably have errors and uncertainties.

[4] The aim of this paper is to assess the E-OBS data set with respect to some of the potential errors that may be present. Users can then familiarize themselves with the strengths and weaknesses of the data and use them responsibly. We choose three important properties of E-OBS to analyze in this paper: (1) homogeneity of the gridded data; (2) inaccuracies due to the underlying station network density, through comparison with existing data sets that have been developed with much denser station networks; and (3) the accuracy of the estimates of interpolation uncertainty that are provided as part of E-OBS.

[5] Long-term station data are often influenced by nonclimatic factors, such as changes in station location or environment, instruments and observing practices. These so-called inhomogeneities can often lead to misinterpretations of the climate data analyzed [*Peterson et al.*, 1998]. The station data used for E-OBS are not fully homogenized. Individual station series may have been homogenized by the original custodians of each series, but the series provided by partner organizations have been used directly, meaning potentially inhomogeneous stations may be contributing to the interpolated grids. As station density strongly influences the interpolation [*Hofstra et al.*, 2008], E-OBS was constructed using many potentially inhomogeneous stations, as their exclusion would degrade the station network density and hence accuracy of the interpolation. In addition, several studies explain that, for area averages of relatively large areas, inhomogeneities balance out during interpolation [*Dai et al.*, 1997; *New*, 1999; *Peterson et al.*, 1998]. However, that may not be the case for the E-OBS high-resolution grids. Therefore, the first out of three properties tested is the homogeneity of the data set.

[6] The second topic is a comparison with other gridded data sets that have been developed with much denser station networks. These data sets are available, in the case of precipitation, for long periods for the UK and the Alps and for the period October 1999 to December 2000 for Europe as a whole. For temperature, unfortunately, we have only been able to secure data for the UK. Data sets developed with denser station networks are assumed to be a better approximation of the true area averages. So if the E-OBS gridded data set produces grid area averages that are close to those calculated from the higher-quality grids, the E-OBS data set can be deemed to be a reasonable representation of the true area-average gridded values.

[7] Because of the inevitable interpolation uncertainties, the E-OBS data set has been provided with information on the interpolation uncertainty for each grid box and each day [*Haylock et al.*, 2008]. E-OBS interpolation uncertainty has been derived by combining the Bayesian standard error estimates of the monthly climatology [*Hutchinson*, 1995] and the interpolation standard deviation for daily anomalies [*Yamamoto*, 2000] (see section 5 for more detail). Here we concentrate on the interpolation standard error estimates, and evaluate the accuracy of the estimates through cross validation against station data. This represents the first evaluation of the *Yamamoto* [2000] standard error method, which has to date only been applied to geological data.

[8] The remainder of the paper is structured as follows. Section 2 provides a more detailed description of the E-OBS data set, including the underlying station data and the interpolation and gridding methodology. We then cover each of the three evaluations in turn: inhomogeneities (section 3), comparison against regional gridded data sets based on denser station networks (section 4) and evaluation of the interpolation standard error estimates (section 5). We conclude with a summary of results and a discussion of the implications of our assessment for use of the E-OBS data set.