Abstract.A high-resolution daily gridded precipitation dataset was built from raw data of 12 858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary islands. The original data were quality-controlled and gaps were filled on each day and location independently. Using the serially complete dataset, a grid with a 5 × 5 km spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterise the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The Mediterranean coast and the Central Range showed the highest frequency and intensity of extreme events, while the number of wet days and dry and wet spells followed a north-west to south-east gradient in peninsular Spain, from high to low values in the number of wet days and wet spells and reverse in dry spells. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that is difficult to achieve when using other methods, pre-selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at https://doi.org/10.20350/digitalCSIC/7393.

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An analysis of the spatial and temporal variability of daily precipitation concentration (CI) in Spain was made based on a high‐resolution (5 × 5 km) daily gridded precipitation data set for the 1950–2012 period. For each grid point in the Iberian Peninsula (IP) and Balearic and Canary Islands, the average annual CI was computed, as well as its coefficient of variation and the 5th and 95th percentiles. Annual values were also computed, and the time series of the index were used to assess temporal trends over the whole period. The spatial distribution of the CI showed a strong relationship with the orographic barriers near the coastlines. The Canary Islands showed the highest values of CI, along with the eastern Mediterranean facade of the IP. The highest inter‐annual variations of the CI occurred in the southern IP and in the southern Canary Islands. The trends of CI were, overall, positive and significant, which indicates an increase of daily precipitation concentration over the study period and an increasing environmental risks scenario where erosivity, torrentiality, and floods may become more frequent.

The growth of past, present, and future forests was, is and will be affected by climate variability. This multifaceted relationship has been assessed in several regional studies, but spatially resolved, large-scale analyses are largely missing so far. Here we estimate recent changes in growth of 5800 beech trees (Fagus sylvatica L.) from 324 sites, representing the full geographic and climatic range of species. Future growth trends were predicted considering state-of-the-art climate scenarios. The validated models indicate growth declines across large region of the distribution in recent decades, and project severe future growth declines ranging from −20% to more than −50% by 2090, depending on the region and climate change scenario (i.e. CMIP6 SSP1-2.6 and SSP5-8.5). Forecasted forest productivity losses are most striking towards the southern distribution limit of Fagus sylvatica, in regions where persisting atmospheric high-pressure systems are expected to increase drought severity. The projected 21st century growth changes across Europe indicate serious ecological and economic consequences that require immediate forest adaptation.

This work presents a method for the reconstruction of fragmentary daily 13 precipitation datasets. The method aims to preserve the local and temporal variability 14 characteristic of high-frequency precipitation data, and does not use the time-structure of 15 the data. Based on the precipitation values recorded at closest neighbours during a target 16 day, two reference values (RV) are computed: a binomial prediction (BP) expressing the 17 probability of occurrence of a wet day; and a magnitude prediction (MP), referring to the 18 amount of precipitation. Generalised linear models (GLM) are used to compute the RV 19 using the precipitation data (occurrence and magnitude) of the 10 nearest neighbours as 20 the dependent variable, and the geographic information of each station (latitude, 21 longitude, and altitude) as the independent variables. The RV are then used to: (1) apply 22 * Corresponding author: rs@unizar.es Climate Research 73 (3): 167-186 (2017) daily precipitation dataset of the island of Majorca in Spain, spanning the period from 29 1971 to 2014. 30 31

Daily precipitation datasets are usually large, bulky and hard to handle, but they are of key importance in many environmental studies. We developed a tool to create custom datasets from observed daily precipitation records. Reference values (RV) are computed for each day and location using multivariate logistic regression with altitude, latitude and longitude as covariates. The operations were compiled in an Open Source R package called reddPrec. The reddPrec package consists of a set of functions used to: i) apply a comprehensive quality control over original daily precipitation datasets, flagging suspect data based on five predefined criteria; ii) fill missing values in original data series by estimating precipitation values using the 10 nearest observations for each day; and iii) create new series and gridded datasets in locations where no data were recorded.

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Aim: Most studies focusing on the alpine tree line responses to climate warming have used either the tree densification within the ecotone or its elevational upshift as indicators. However, it is acknowledged that the relationship between densification and upshift is spatially heterogeneous, making inferences and comparability among studies tricky. The lack of consistent empirical evidence on this potential mismatch and its drivers leads us to focus on this issue in this study. The aim was twofold: (a) to quantify the mismatch between the two processes at a regional scale, and (b) to identify its site-specific determinants. Taxon: Pinus uncinata (Ramond ex DC.)Location: French eastern Pyrenees.Methods: An object-oriented supervised classification procedure was performed on historical (1953) and current (2015) aerial photographs. Based on the resulting rasters, densification of the tree line ecotone and upward shift of the tree line were estimated at the two dates in 191 sites, then standardized, before finally being compared. Three site clusters were derived (no mismatch, densification prevalence and upshift prevalence). After having characterized their spatial patterns through join count statistics, a multinomial logistic regression model was computed to identify the correlates of these clusters among a list of site variables. Results:No spatial pattern among the categories of responses emerges at a local scale, but buffers with no mismatch tend to aggregate at a larger scale. Changes in minimum air temperatures, site elevation, mean slope, slope morphometry and lithology appear as significant drivers of the observed mismatch, implying that the relationship between densification and elevational upshift is context specific. Main conclusions:Our findings suggest that both densification and upshift should be considered in quantitative analyses of tree line spatial dynamics, since these two ecological processes are not controlled by the same drivers. K E Y W O R D S densification, join count statistics, multinomial model, spatial context, tree line, upshift | 1057 FEUILLET ET aL. | 1067 FEUILLET ET aL.