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.

Significance: Future large tropical volcanic eruptions will induce global hydroclimatic changes, superimposed on anthropogenic climate change. Understanding how volcanic eruptions affect global hydroclimate is therefore critically important. Tejedor et al. use a new paleoclimatic product, which combines information from high-resolution proxies and climate models, to estimate volcanic impacts on hydroclimate over the last millennium. They find that past eruptions caused severe drying in tropical Africa and across Central Asia and the Middle East and significantly wetter conditions over Oceania and the South American monsoon region, some of which persisted for a decade or longer. These proxy-based findings suggest that, relative to estimates from a state-of-the-art climate model, much larger and persistent hydroclimatic changes are possible across regions of important socioeconomic activity.

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 2022 and 2023 western Mediterranean summer temperatures exceeded millennial natural variability, reaching unprecedented anomalies of +3.6 °C and +2.9 °C respectively. We show that anthropogenic climate change may turn extreme heatwaves from a rarity of 1 in 10,000 years into events occurring every 4–75 years, depending on future scenarios. This shift underscores the urgency of implementing adaptive strategies as extreme climate events manifest sooner and more intensely than expected.