19 Pags.- 11 Figs.- 2 Tabls. © The Authors. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License., The analysis of monthly air temperature trends over mainland Spain during1916–2015 shows that warming has not been constant over time nor general-ized among different months; it has not been synchronous for maximum andminimum air temperatures; and it has been heterogeneous in space. Tempera-ture rose during two characteristic pulses separated by a pause around themiddle of the 20th century in some months. In other months, only the secondrising period is identified, or no warming can be found. In all months, andboth for maximum and minimum air temperatures, a stagnation of theincreasing trend is observed in the last two decades of the study period. Highspatial variability exists in trend signal and significance, and two contrastingtemporal patterns of advance over the study area are identified for maximumand minimum air temperatures. These patterns can be related to prevalentflow directions and relief disposition with respect to the flows associated withlow-variability meteorological patterns North Atlantic Oscillation (NAO) andWestern Mediterranean Oscillation (WEMO). The results show that warmingis a complex phenomenon at regional and sub-regional scales that can only beanalysed using high-spatial-resolution data and considering global and localfactors., This study was funded by Spanish Government, Ministryof Economy and Competitiveness, CLICES project (CGL2017-83866-C3-1-R, CGL2017-83866-C3-3-R) and Ministry of Science and Innovation, EXE project (PID2020-116860RB-C22); Regional Government of Aragon, Geoenvironmental and Global Climate Change Research Group (E02-17R). Dhais Peña-Angulo received a “Juan de la Cierva” postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC)., Peer reviewed

[EN] A monthly precipitation gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations. Spatial interpolation was performed using geostatistical techniques over a regular 0.1° × 0.1° grid, using a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 11.312 observatorios. Se utilizaron técnicas geoestadísticas para interpolar espacialmente las observaciones sobre una rejilla regular de 0.1° × 0.1°, utilizando un proceso en dos pasos: en primer lugar se interpoló la probabilidad de mes seco (precipitación igual a cero), y en un segundo paso la magnitud de la precipitación., Projects CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland) and PID2020-116860RB-C22 EXE: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Mean grid; standard deviation grid; netCDF., No

[EN] 1. Compressed folder ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip'): 40 folders with the graphic description of each event, numbered from 1 to 40 and with their respective start and end dates (e.g. '19_1973_oct-1974_feb'). In each of them, three folders: 'maps' with the integral maps of duration and intensity (.png format), 'seq_ano' with the maps of the spatial spread (.png format) and 'ts' with the averages of SPI01 and SPI12 and the area affected by the drought (SPI12 =< -0.84) (.png format). Map legends (.png format). .xlsx file showing the identification and characterization of the events. 2. Compressed folder ('Drought_events_characterization_code.zip'): project, script and folder with the data necessary to generate the descriptive analysis material in R. 3. netCDF file ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc'). Variables (6): 'SPI-01' (1-month Standardized Precipitation Index), 'SPI-03' (3-month Standardized Precipitation Index), 'SPI-06' (6-month Standardized Precipitation Index), 'SPI-12' (12-month Standardized Precipitation Index), 'SPI-36' (36-month Standardized Precipitation Index) and 'evnt' (Drought episodes). Temporal extent: 1272 (months from 1915-01 to 2020-01). Spatial extent: longitude = 125, latitude: 77, units = 0.1 degrees. Reference system: EPSG 4326, WGS84 projection, geographic coordinates. Open Data Commons Attribution (ODC-By 1.0)., [EN] The database consists of a netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') and two zipped folders ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' and 'Drought_events_characterization_code.zip'). The first folder includes a descriptive analysis of the 40 drought episodes identified according to the criteria of drought intensity (SPI12 =< -0.84) and area affected (>20 % of the grid area). For each episode, the time series of the SPI01 and SPI12 average of the whole grid (expressed in anomalies); the area of the grid under drought conditions (SPI12 =< -0.84) (expressed in percent per one); the integral maps of the episode according to its duration (expressed in number of months) and intensity (average of the cells under drought conditions); and the maps representing the spatial propagation of the episode are included. The second folder includes the code necessary to generate the descriptive analysis material. The netCDF consists of six variables. The first five are the precipitation anomalies (Standardized Precipitation Index at 1 month, 3, 6, 12 and 36 time scales: 'SPI-01', 'SPI-03', 'SPI-06', 'SPI-12' and 'SPI-36'), these have been obtained from the monthly data of the MOPREDAScentury precipitation grid (https://doi.org/10.20350/digitalCSIC/15136). The sixth variable is the drought events themselves ('evnt'), numbered from 1 to 40. These six variables refer to the Spanish peninsular territory and the time period 1915/01-2020/01. This record corresponds to version 1.0.0 of the dataset. The database is distributed under an open license (Open Data Commons Attribution, ODC-By)., [ES] La base de datos consta de un netCDF ('Spanish_Drought_Catalogue_v1.0.0-SPI_grids.nc') y dos carpetas comprimidas ('Spanish_Drought_Catalogue_v1.0.0-Drought_episodes.zip' y 'Drought_events_characterization_code.zip'). En la primera carpeta se incluye un análisis descriptivo de los 40 episodios de sequía identificados según los criterios de intensidad de la sequía (SPI12 =< -0.84) y superficie afectada (>20 % de la superficie de la malla). Para cada episodio se incluyen las series temporales del SPI01 y SPI12 promedio de toda la malla (expresadas en anomalías); el área de la malla en condiciones de sequía (SPI12 =< -0.84) (expresada en tanto por uno); los mapas integrales del episodio atendiendo a su duración (expresada en número de meses) e intensidad (promedio de las celdas en condiciones de sequía); y los mapas que representan la propagación espacial del episodio. En la segunda carpeta se incluye el código necesario para generar el material del análisis descriptivo. El netCDF consta de seis variables. Las cinco primeras son las anomalías de precipitación (Standardized Precipitation Index a escala temporal de 1 mes, 3, 6, 12 y 36: ‘SPI-01’, ‘SPI-03’, ‘SPI-06’, ‘SPI-12’ y ‘SPI-36’), estas han sido obtenidas a partir de los datos mensuales de la malla de precipitación MOPREDAScentury (https://doi.org/10.20350/digitalCSIC/15136). La sexta variable son los eventos de sequía propiamente dichos (‘evnt’), numerados del 1 al 40. Estas seis variables están referidas al territorio peninsular español y el periodo temporal 1915/01-2020/01. Este registro se corresponde con la versión 1.0.0 del conjunto de datos. La base de datos se distribuye bajo una licencia abierta (Open Data Commons Attribution, ODC-By)., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., No

13 Pags.- 6 Figs.- 2 Tabls. © 2023 The Authors., Due to its geographical location in the western Mediterranean region, the Iberian Peninsula involves a challenge for current climatic conditions and future projections. In this study we analysed monthly precipitation trends over mainland Spain from 1916 to 2020 by using the new MOPREDAS_century database. This database combines information from the Spanish Meteorological Agency's archives, as well as data retrieved from Annual Summaries between 1916 and 1950. A combination of both sources produced the largest amount of original information ever collected and researched in mainland Spain between 1916 and 2020., Financial support was provided by Spanish Government, Ministerio de Ciencia y Competitividad-FSE, Projects PID2020-116860RB-C22 and CGL2017-83866-C3-1-R. Regional Government of Aragon, Geoenvironmental and Global Climate Change Research Group, Grant/Award Number: E02-17R., Peer reviewed

29 Pags.-, 24 Figs.- 15 Tabls. © Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License., This article describes the development of a monthly precipitation dataset for the Spanish mainland,
covering the period between December 1915 and December 2020. The dataset combines ground observational
data from the National Climate Data Bank (NCDB) of the Spanish meteorological service (AEMET) and new
data rescued from meteorological yearbooks published prior to 1951 that were never incorporated into the
NCDB. The yearbooks’ data represented a significant improvement of the dataset, as it almost doubled the
number of weather stations available during the first decades of the 20th century, the period when the data were
more scarce. The final dataset contains records from 11 312 stations, although the number of stations with data
in a given month varies largely between 674 in 1939 and a maximum of 5234 in 1975. Spatial interpolation was
used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process:
estimation of the probability of zero precipitation (dry month) and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly
climatology) as dependent variables and several geographic variates as independent variables. Cross-validation
results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the
variance deflation effect are highest during the first decades of the 20th century, when the observational data
were more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license
and can be cited as Beguería et al. (2023)., . This research has been supported by research
grants PID2020-116860RB-C22, CGL2017-83866-C1-3-R, and
CGL2017-83866-C3-3-R by the Spanish Ministry of Science and
Innovation and by the European Commission – NextGenerationEU
(Regulation EU 2020/2094), through CSIC’s Interdisciplinary
Thematic Platform Clima (PTI Clima)/Development of Operational
Climate Services., Peer reviewed

[ES] Dos archivos en formato .csv ('Infogeo_Spain10.csv' y 'MOPREDAScentury_PP_1916-2020.csv'). El primero de ellos contiene los metadatos de los puntos del grid, la primera columna es el ID (Point), la segunda es la latitud (lon), la tercera es la latitud (lat) y la cuarta es la elevación del punto respecto al nivel del mar (alt.). En el segundo archivo queda recogida la información climática, en la primera columna queda recogido el ID (Point), en la segunda el año al que van referidos los datos (Year) y las 12 columnas siguientes se muestran las precipitaciones acumuladas a nivel mensual de enero a diciembre (Jan a Dec) y expresadas en décimas de mm. [EN] Two files in .csv format ('Infogeo_Spain10.csv' and 'MOPREDAScentury_PP_1916-2020.csv'). The first one contains the metadata of the grid points, the first column is the ID (Point), the second is the latitude (lon), the third is the latitude (lat) and the fourth is the elevation of the point with respect to sea level (alt.). In the second file the climatic information is collected, in the first column is the ID (Point), in the second column the year to which the data refer (Year) and the following 12 columns show the accumulated precipitation at monthly level from January to December (Jan to Dec) and expressed in tenths of mm., [EN] A monthly precipitation gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations. Spatial interpolation was performed using geostatistical techniques over a regular 0.1° × 0.1° km grid, using a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 11.312 observatorios. Se utilizaron técnicas geoestadísticas para interpolar espacialmente las observaciones sobre una rejilla regular de 0.1° × 0.1°, utilizando un proceso en dos pasos: en primer lugar se interpoló la probabilidad de mes seco (precipitación igual a cero), y en un segundo paso la magnitud de la precipitación., Projects CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland) and PID2020-116860RB-C22 EXE: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Infofile, grid., No

16 Pags.- 8 Figs.- 3 Tabls. The data that support the findings of this study are openly available in https://digital.csic.es/handle/10261/341441?mode=full at http://hdl.handle.net/10261/341441. This is an open access article under the terms of the Creative Commons Attribution License., This study examines the spatial and temporal variations in seasonal precipitation patterns across the Spanish mainland, within the western Mediterranean Basin, spanning the years 1916–2015. Utilizing the recently developed MOPREDAS_century database at a 10 × 10 km grid resolution, our analysis reveals predominantly negative trends in precipitation across the study area over the entire period, primarily driven by declines in spring precipitation, with lesser decreases in summer and winter. Notably, these trends exhibit complexity, manifesting as two distinct pulses occurring roughly at the outset and conclusion of the 20th century. However, upon employing a moving windows analysis, we find that since the mid-1970s, seasonal precipitation trends have largely been nonsignificant. Spatially, the precipitation regime across 1916–2015 delineates three primary zones: a winter-maximum zone in the north, an autumn-maximum zone along the eastern coast, and a transition from winter-maximum to spring-maximum towards the western and inland regions. Noteworthy shifts in this spatial distribution are evident over the four 25-year intervals examined. Initially, during 1916–1940, winter, autumn and spring regimes occupied 44.1%, 24.2% and 31.7% of the land, respectively. Subsequently, in the periods 1941–1965 and 1966–1990, winter dominance expanded to over 50% of the grid area, while spring varied between 34.7% and 25.5%, and autumn decreased notably to 12.4% and 13.5%. Lastly, significant alterations occurred in the final period of 1991–2015, with winter coverage decreasing to 33.7%, spring to 15.8% and autumn expanding to 50.1% of the grid area. In summary, the prevalent winter and spring regimes at the dawn of the 20th century have transitioned towards an autumn-dominated regime, particularly in much of the central and western Spanish mainland, with these shifts possibly linked to the evolving precipitation trends. These findings contribute novel insights, particularly pre-1950, and align with published results post-1950 across the Mediterranean, particularly in its western regions. Furthermore, they suggest a potential connection between changes in the spatial distribution of seasonal precipitation regimes and temporal variations in the primary moisture sources over the study area., Financial support was provided by Regional Government of Aragon, Geoenvironmental and Global Climate Change Research Group, Grant/Award Number: E02-17R; Spanish Government, Ministerio de Ciencia e Innovación-FSE, Projects: PID2020-116860RB-C22 (AEI/10.13039/501100011033), CGL2017-83866-C3-1-R., Peer reviewed

Drought is the most devastating natural hazard in terms of its spatial extent, duration, and potential to affect whole regions of the world. Its
impacts are first noticed in the natural and agro-ecosistems but are rapidly expanded towards other economic sectors, ultimately affecting the
whole society specially in less developed countries. Despite its importance, up to now there is little knowledge of the mechanisms that drive
drought onset and evolution. One possible way of studying droughts is by developing a catalogue of past episodes and studying their
progressive development and the atmospheric mechanisms that played a role. Here, we leveraged on the longest and most dense data base of
monthly precipitation and temperature over mainland Spain covering 1916-2020, previously developed by our team, to study the
largest drought episodes over the last century. We computed a data set of drought indices (SPI, SPEI) to determine the largest drought events of
the last century for further analysis. Each event was assessed in terms of its temporal variability considering short and long-scale of the drought
indices and their spatial evolution, and were characterised by their integral magnitudes (intensity, duration). Principal components analysis was
used to find the main spatial configurations of drought over the study area. Finally, each spatial component was related to their main
atmospheric drivers. As a result, we elaborated a catalogue of drought events in Spain over the last century, and to infer several characteristic
types. The longest and most intense episodes, though, were usually characterised by a complex behaviour with a succession of more than one
type., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Peer reviewed

[ES] "Summaries_of_meteorological_observations_from_the_Spanish_Meteorological_Service, v.2.0.0. [Dataset]" se compone de los datos de precipitación (total mensual y maximo diario mensual) y temperatura (media mensual de las maximas diarias, media mensual de las minimas diarias, maximo diario mensual, minimo diario mensual) de los Libros Resúmenes Anuales editados por los sucesivos Servicios Meteorológicos de España en el periodo 1915-1950. No se ha aplicado ningún procedimiento de tratamiento a los datos, son datos brutos lo más exactos posible procedentes de los Anuarios. El procedimiento de cotejo entre las observaciones de los Anuarios (LRAs) y las de la Base Nacional de Datos del Clima (BNDC) consistió en determinar, para cada estación que aparece en los LRAs, la estación a la que corresponde en la BNDC. No se trata de una tarea sencilla, ya que el nombre de un mismo observatorio en los LRAs puede aparecer de forma diferente en años sucesivos, mientras que, por otra parte, la fiabilidad de las coordenadas es escasa. En consecuencia, el cotejo en su primera fase se convirtió en una combinación de identificación de nombres de observatorio similares entre ambas fuentes junto con la búsqueda de cadenas de registros idénticas en la secuencia mensual de cada año. En las series anuales así identificadas como idénticas (o muy similares), se comprobó que tenían la misma localización provincial y que el nombre del observatorio era similar en ambas fuentes, teniendo en cuenta que su nombre puede aparecer de forma diferente a lo largo de los años. En una segunda fase, la información no emparejada por el proceso anterior se trató individualmente para cada caso (registro anual), buscando similitud de nombre y provincia si ampliaba series ya emparejadas, o si podía considerarse una nueva serie no incluida en la BNDC, en cuyo caso se estimaron sus coordenadas con precisión de 1 minuto de latitud y longitud. [EN] "Summaries_of_meteorological_observations_from_the_Spanish_Meteorological_Service, v.2.0.0. [Dataset]" is composed of precipitation (monthly total and monthly daily maximum) and temperature (monthly mean daily maximum, monthly mean daily minimum, monthly daily maximum, monthly daily minimum) data from the Annual Summary Books published by the successive Spanish Meteorological Services in the period 1915-1950. No processing procedure has been applied to the data, they are raw data as accurate as possible from the Annual Books (ABs). The matching procedure between the observations of the ABs and those of the National Climate Database (NCDB) consisted in determining, for each station appearing in the ABs, the station to which it corresponds in the NCDB. This is not a simple task, since the name of the same observatory in the ABs may appear differently in successive years, while, on the other hand, the reliability of the coordinates is poor. Consequently, the matching in its first phase became a combination of identification of similar observatory names between both sources together with the search for identical record strings in the monthly sequence of each year. In the annual series thus identified as identical (or very similar), it was found that they had the same provincial location and that the observatory name was similar in both sources, taking into account that its name may appear differently over the years. In a second phase, the information not matched by the previous process was treated individually for each case (annual record), looking for similarity of name and province if it extended series already matched, or if it could be considered a new series not included in the NCBD, in which case its coordinates were estimated with 1-minute precision of latitude and longitude., [ES] El conjunto de datos "Summaries_of_meteorological_observations_from_the_Spanish_Meteorological_Service" es el resultado de un proceso de digitalización de los datos publicados en los Resúmenes de las Observaciones Meteorológicas -realizados a nivel anual- y editados por el Servicios Meteorológico Español, periodo 1915-1950. Concretamente, la información digitalizada es la precipitación mensual y la precipitación máxima diaria mensual junto a la media mensual de las temperaturas máximas diarias, la media mensual de las temperaturas mínimas diarias y el valor máximo y el valor mínimo diario de temperatura a nivel mensual. Estos datos, combinados con los incluidos en el BNDC, han sido empleados para el desarrollo de las mallas de alta resolución denominadas MOPREDAS y MOTEDAS_1916-2015 y subsiguientes versiones, [EN] The data set "Summaries_of_meteorological_observations_from_the_Spanish_Meteorological_Service" is the result of a digitization process of the data published in the Summaries of Meteorological Observations -made at annual level- and edited by the Spanish Meteorological Service, period 1915-1950. Specifically, the digitized information is the monthly precipitation and the maximum daily monthly precipitation together with the monthly mean of the maximum daily temperatures, the monthly mean of the minimum daily temperatures and the maximum and minimum daily values of temperature at monthly level. These data, combined with those included in the BNDC, have been used for the development of the high-resolution grids called MOPREDAS and MOTEDAS_1916-2015 and subsequent versions., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., No

[ES] El catálogo incluye tantos las fechas de los eventos como las del día previo y posterior en un listado junto al mapeado de cada uno de ellos en cartografías individuales (3 por cada evento) donde se incluyen todos los observatorios en la misma fecha que registraron el máximo mensual de precipitación, fuese cual fuese su magnitud. [ES] The catalogue includes the dates of the events as well as those of the previous and following day in a list together with the mapping of each of them in individual cartographies (3 for each event) where all the observatories on the same date that recorded the maximum monthly precipitation, whatever its magnitude, are included., [ES] "Extraordnary_Daily_Precipitation_Catalogue_of_Spain_1916-2022, v.1.0.0. [Dataset]" es un catálogo de eventos de lluvia extraordinaria sucedidos a escala diaria superiores a 100 mm y 200 mm obtenido a partir de los Libros Resúmenes Anuales, editados por los sucesivos Servicios Meteorológicos de España en el periodo 1916-1950 y los fondos documetales del Banco Nacional de Datos Climáticos de AEMET., [EN] “Extraordinary_Daily_Precipitation_Catalogue_of_Spain_1916-2022, v.1.0.0. [Dataset]” is a catalog of daily extraordinary rainfall events greater than 100 mm and 200 mm obtained from the Annual Summary Books, edited by the successive Meteorological Services of Spain in the period 1916-1950 and the documentary collections of the National Climatic Data Bank of AEMET., Project PID2020-116860RB-C22: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science, No

13 Pags.- 7 Figs.- 1 Tabl. Data availability: All data generated and evaluated in this publication are available at DIGITAL.CSIC repository (https://doi.org/10.20350/digitalCSIC/15446). Code availability: Code used in the 40 drought events characterization is available at DIGITAL.CSIC (https://doi.org/10.20350/digitalCSIC/15446).
© The Author(s) 2024. This article is licensed under a Creative Commons Attribution 4.0 International License., We leveraged the most extensive and detailed gridded database of monthly precipitation data across the Spanish mainland (MOPREDAScentury), encompassing 1916–2020 time period, to pinpoint the most severe drought events within this timeframe and analyse their spatio-temporal dynamics. To identify these events, we employed the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were identified as periods of at least three months where significantly dry conditions affected 20% or more of the study area, defined as grid cells with SPI values lower than −0.84. Our analysis revealed a total of 40 major drought events. Our catalogue contains detailed information on each episode’s spatial extent, duration, severity, and spatio-temporal dynamics. The analysis of the propagation patterns of the events unveils substantial heterogeneity, implying that droughts stem from diverse atmospheric mechanisms, further influenced by complex local topography. The open-licensed drought database serves as a valuable resource. It not only facilitates exploration of drought onset and evolution mechanisms but also aids in assessing drought impact on agricultural and other socio-economic sectors., This research has been supported by research grants PID2020-116860RB-C22, CGL2017-83866-C1-3-R, and CGL2017-83866-C3-3-R by the Spanish Ministry of Science and Innovation and by the European Commission – NextGenerationEU (Regulation EU 2020/2094), through CSIC’s Interdisciplinary Thematic Platform Clima (PTI Clima)/Development of Operational Climate Services. We acknowledge support of the Aragón regional Government through grant “E02_23R: Procesos geoambientales y Cambio Global”., Peer reviewed

Flash droughts (FDs) are distinguished by a rapid development associated with strong precipitation deficits and/or increases in atmospheric evaporative demand in the short-term, but little is known about the atmospheric conditions underlying these events. In this study, we analyse for the first time the atmospheric dynamics involved in the development of FDs in Spain over the period 1961–2018. FDs are related to large-scale atmospheric circulation patterns affecting the region, in particular with the positive phase of the North Atlantic Oscillation (NAO). The NAO is the main atmospheric driver of FDs in winter and autumn, and it is essential in explaining FD development in spring. We also found that FDs are typically linked to strong positive anomalies in 500 hPa geopotential height and sea level pressure over the region during the weeks prior to the onset. At the synoptic scale, the most common weather types (WTs) recorded during the development of FDs are Anticyclonic Western (ANT_W_AD), East (E_AD) and Northeast (NE_AD) advection, and Anticyclonic (ANTICYC). In particular, ANTICYC WT is the main atmospheric driver of FDs in summer. Ridging conditions occur frequently during FDs in all seasons, being the most important factor controlling FD development in spring. Likewise, we noted that some of the FDs recorded in summer are related to and/or exacerbated by Saharan air intrusions associated with pronounced ridges. The results of this research have important implications for the understanding, monitoring and prediction of FDs in Spain, providing a detailed assessment of the main atmospheric dynamics involved in FD triggering at different spatial scales., This work has been supported by the research projects: TED2021-129152B-C41, PID2020-116860RB-C22 and PID2022-218 137244OB-I00 financed by the Spanish Ministry of Science, Innovation and Universities., Peer reviewed

[ES] Esta base de datos climática presenta un conjunto de información representado en forma de cuadrícula a escalas mensual, estacional y anual sobre la aridez en España (territorios de la Península Ibérica, islas Baleares e islas Canarias) entre enero de 1961 y la actualidad. El Índice de Aridez es una medida del carácter seco o húmedo del clima de una región, y junto con la temperatura media es uno de los factores climáticos que mejor explican la distribución de los diferentes biomas en el planeta o la potencialidad para el cultivo. Aquí, se ha elaborado una base de datos reticular del Índice de Aridez definido por el Programa de Naciones Unidas para el Medio Ambiente: (IA, mm / mm) como el cociente entre la precipitación (P, mm) y la evapotranspiración potencial (PET, mm): AI = P / PET. En lugar de PET se ha utilizando la ETo (evapotranspiración de referencia), calculada mediante la ecuación Penman-Monteith de la FAO-56, y que ofrece una medida más adecuada de la evaporación potencial. Los datos de P y ETo se han obtenido de la Base de Datos Histórica de la Sequía en España (https://monitordesequia.csic.es/historico), desarrollada a partir de los datos de observación meteorológica del Banco Nacional de Datos Climáticos de AEMET, interpolados espacialmente para cubrir la totalidad del territorio. Se han calculado tanto las series temporales como las climatologías mensuales, estacionales y anuales del Índice de Aridez. Para el cálculo de climatologías (valores medios) se ha considerado el periodo 1961-2020, cubriendo 60 años. Este marco temporal se ha subdivido además en dos periodos iguales de 30 años, conocidos como periodos normales: 1961-1990 y 1991-2020, lo que permite analizar cambios en el valor del Índice de Aridez. Este índice se puede consultar de forma cuantitativa o bien categorizado en clases, siendo esta la forma de visualización por defecto. Junto a la información histórica y la actualizada anualmente, se incluyen las proyecciones futuras de la aridez climática en España (territorios de la Península Ibérica, Baleares y Canarias) para las Trayectorias socioeconómicas compartidas (SSP, por sus siglas en inglés) SSP 126, SSP 245, SSP 370 y SSP 585 y los niveles de calentamiento global (GWL, por sus siglas en inglés) +1,5 ºC, +2 ºC, +3 ºC y +4 °C. Los productos generados incluyen las climatologías (promedios y medianas de los modelos), el grado de acuerdo (considerándolo solamente cuando más 80 % de los modelos presenten el mismo signo de cambio) y los cambios relativos (diferencias entre los periodos de 20 años alrededor de cada GWL y la climatología del periodo de referencia 1986-2014).

[EN] This climatic database presents a set of information represented in a grid form at monthly, seasonal and annual scales on aridity in Spain (territories of the Iberian Peninsula, Balearic Islands and Canary Islands) between January 1961 and the present. The Aridity Index is a measure of the dry or humid character of the climate of a region, and together with the average temperature is one of the climatic factors that best explain the distribution of the different biomes on the planet or the potential for cultivation. Here, a grid database of the Aridity Index defined by the United Nations Environment Program (AI, mm / mm) has been elaborated as the quotient between precipitation (P, mm) and potential evapotranspiration (PET, mm): AI = P / PET. Instead of PET, ETo (reference evapotranspiration), calculated using the Penman-Monteith equation of FAO-56, has been used, which provides a more adequate measure of potential evaporation. The P and ETo data were obtained from the Historical Database of Drought in Spain (https://monitordesequia.csic.es/historico), developed from the meteorological observation data of the National Climatic Data Bank of AEMET, spatially interpolated to cover the entire territory. Both the time series and the monthly, seasonal and annual climatologies of the Aridity Index have been calculated. For the calculation of climatologies (average values), the period 1961-2020 has been considered, covering 60 years. This time frame has been further subdivided into two equal periods of 30 years, known as normal periods: 1961-1990 and 1991-2020, which allows us to analyze changes in the value of the Aridity Index. This index can be consulted quantitatively or categorized into classes, this being the default display. Along with the historical and annually updated information, future projections of climate aridity in Spain (territories of the Iberian Peninsula, Balearic Islands and Canary Islands) are included for the Shared Socioeconomic Pathways (SSP) SSP 126, SSP 245, SSP 370 and SSP 585, and the Global Warming Levels (GWL) +1.5 ºC, +2 ºC, +3 ºC and +4 °C. The products generated include the climatologies (averages and medians of the models), the degree of consensus (only considering it when at least 80% of the models show the same sign of change) and the relative changes (differences between the periods of 20 years around each GWL and the climatology of the reference period 1986-2014)., [ES] Este conjunto de datos proporciona información en tiempo cuasi real (actualización anual) de las condiciones de aridez climática en España a resolución espacial de 1.1 km. Proporciona los valores del Índice de Aridez de la FAO a diferentes escalas temporales (resolución temporal mensual, estacional y anual) desde el año 1961 hasta la actualidad junto a las proyecciones futuras de este índice asociadas a distintos niveles de calentamiento global (GWLs) y escenarios de emisión (SSPs)., [EN] This dataset provides near real-time information (annual update) of climatic aridity conditions in Spain at a spatial resolution of 1.1 km. It provides the FAO Aridity Index values at different time scales (monthly, seasonal and annual temporal resolution) from 1961 to the present, along with future projections of this index associated with various Global Warming Levels (GWLs) and Shared Socieconomic Pathways (SSPs)., This work has been supported by the Biodiversity Foundation of the Ministry for the Ecological Transition and the Demographic Challenge (MITECO) of the Government of Spain, within the framework of the Recovery, Transformation and Resilience Plan (PRTR), funded by the European Union - NextGenerationEU: Project I023, Analysis and monitoring of climatic aridity in Spain (ARIDESP). We also acknowledge financial support from the Ministry of Science, Innovation and Universities of the Government of Spain through research project PID2020-116860RB-C22, and from the Government of Aragón through grant E02-17R., No

21 Pags.- 10 Figs.- 5 Tabls. Data Availability Statement: The data that support the findings of this study are openly available in DIGITAL CSIC at http://hdl.handle.net/10261/354840. © 2025 The Author(s). International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License., Catalogues of past hazardous events are essential for understanding their magnitude, frequency, geographical distribution and underlying mechanisms. Such compilations provide critical data for identifying patterns, improving predictive models, and enhancing preparedness, thereby contributing to the mitigation of future risks and facilitating effective adaptation to climate-related hazards. In this article, we present the first comprehensive catalogue of extraordinary precipitation over the Spanish mainland, spanning more than a century. This work provides valuable insights into spatial and temporal patterns of these events. We compiled monthly maxima of daily precipitation (Rx1day) exceeding 100 and 200 mm by combining the documentary collections of the AEMET National Climate Data Bank (1916–2022) with newly digitised data from the Annual Summaries of Observation Books (1916–1950). As a result, this dataset represents the most extensive and detailed collection of extraordinary precipitation events in the region. It includes 19,184 monthly maxima records above 100 mm from 4325 observatories, occurring across 4814 days (events). Additionally, there are 1130 records exceeding 200 mm, grouped into 530 days and recorded in 664 observatories. As anticipated, extraordinary rainfalls were most frequent in autumn, while summer recorded the lowest number of events. These events occurred across nearly all regions, although a higher concentration were observed near coastlines. This is particularly the case of records and events over 200 mm, which concentrated along the Mediterranean arc. We identified five spatial patterns that correspond with specific weather types. Beyond atmospheric circulation, the topography played a critical role in determining the spatial extension of extraordinary precipitations. The newly digitised dataset is available at https://digital.csic.es/handle/10261/354840, accompanied by a collection of maps of daily events. Raw data from the AEMET's National Climate Data Bank are subject to a licenced agreement., This work was supported by Spanish Governement, Ministerio de Ciencia e Innovación-European Social Fund, Grant/Award Numbers: PID2020-116860RB-C22, CGL2017-83866-C3-1-R; Regional Government of Aragon, Geoenvironmental and Global Climate Change Research Group, Grant/Award Number: E02-23R. Geoenvironmental and Global Climate Change Research Group.”, Peer reviewed

14 Pags.- 9 Figs. Data Availability: Statement Data repository, https://doi.org/10.20350/digitalCSIC/16265 (Beguería and Trullenque-Blanco 2024). © 2025 The Author(s). International Journal of Climatology published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License., Climate aridity (the long-term balance between water availability through precipitation and the atmospheric evaporative de-mand) has a fundamental role in determining water availability and the geographic distribution of ecosystems and agriculturalregions, and plays a crucial role in shaping ecological transitions under current climate change. We computed the Aridity Index,computed as the ratio of precipitation to reference evapotranspiration, over Spain for the period 1961–2020. Here we present spa-tially detailed climatologies of the Aridity Index, at the annual and the monthly scales, and an assessment of changes betweenthe normal periods 1961–1990 and 1991–2020. The results show a transition towards reduced values of the Aridity Index (i.e.,towards drier conditions) at the annual scale, which was more intense in the Canary Islands (where 16.3% of the territory tran-sitioned towards more arid climate categories) than in mainland Spain and the Balearic Islands (11.6%). At the monthly level,the most striking changes over mainland Spain occurred in June, with 39.7% of the territory transitioning towards more aridcategories, while transitions towards more humid conditions have only been relevant in March (23.5%) and October (13.0%) anddid not compensate for the aridification trend when the whole year is considered. In the Canary Islands, the strongest changesoccurred in May (22.6%) and September (19.4%), although drying trends were found almost in all months except the summer., This work has been supported by the Biodiversity Foundation of the Ministry for the Ecological Transition and the Demographic Challenge(MITECO) of the Government of Spain, within the framework of the Recovery, Transformation and Resilience Plan (PRTR), funded by the EuropeanUnion—NextGenerationEU. We also acknowledge financial support from the Ministry of Science, Innovation and Universities of the Government of Spain through research project PID2020-116860RB-C22, and from the Government of Aragón through grant E02-17R., Peer reviewed

Due to its geographical location in the western Mediterranean region, the Iberian Peninsula involves a challenge for current climatic conditions and future projections. In this study we analysed monthly precipitation trends over mainland Spain from 1916 to 2020 by using the new MOPREDAS_century database. This database combines information from the Spanish Meteorological Agency's archives, as well as data retrieved from Annual Summaries between 1916 and 1950. A combination of both sources produced the largest amount of original information ever collected and researched in mainland Spain between 1916 and 2020.

This article describes the development of a monthly precipitation dataset for the Spanish mainland, covering the period between December 1915 and December 2020. The dataset combines ground observational data from the National Climate Data Bank (NCDB) of the Spanish meteorological service (AEMET) and new data rescued from meteorological
yearbooks published prior to 1951 that were never incorporated into the NCDB. The yearbooks' data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the data were more scarce. The final dataset contains records from
11 312 stations, although the number of stations with data in a given month varies largely between 674 in 1939 and a maximum of 5234 in 1975. Spatial interpolation was used on the resulting dataset to create monthly precipitation grids. The process involved a two-stage process: estimation of the probability of zero precipitation (dry month) and estimation of precipitation magnitude. Interpolation was carried out using universal kriging, using anomalies (ratios with respect to the 1961–2000 monthly climatology) as dependent variables and several geographic variates as independent variables. Cross-validation results showed that the resulting grids are spatially and temporally unbiased, although the mean error and the variance deflation effect are highest during the first decades of the 20th century, when the observational data were more scarce. The dataset is available at https://doi.org/10.20350/digitalCSIC/15136 under an open license and can be cited as Beguería et al. (2023).

This study examines the spatial and temporal variations in seasonal precipitation patterns across the Spanish mainland, within the western Mediterranean Basin, spanning the years 1916–2015. Utilizing the recently developed MOPREDAS_century database at a 10 × 10 km grid resolution, our analysis reveals predominantly negative trends in precipitation across the study area over the entire period, primarily driven by declines in spring precipitation, with lesser decreases in summer and winter. Notably, these trends exhibit complexity, manifesting as two distinct pulses occurring roughly at the outset and conclusion of the 20th century. However, upon employing a moving windows analysis, we find that since the mid‐1970s, seasonal precipitation trends have largely been nonsignificant. Spatially, the precipitation regime across 1916–2015 delineates three primary zones: a winter‐maximum zone in the north, an autumn‐maximum zone along the eastern coast, and a transition from winter‐maximum to spring‐maximum towards the western and inland regions. Noteworthy shifts in this spatial distribution are evident over the four 25‐year intervals examined. Initially, during 1916–1940, winter, autumn and spring regimes occupied 44.1%, 24.2% and 31.7% of the land, respectively. Subsequently, in the periods 1941–1965 and 1966–1990, winter dominance expanded to over 50% of the grid area, while spring varied between 34.7% and 25.5%, and autumn decreased notably to 12.4% and 13.5%. Lastly, significant alterations occurred in the final period of 1991–2015, with winter coverage decreasing to 33.7%, spring to 15.8% and autumn expanding to 50.1% of the grid area. In summary, the prevalent winter and spring regimes at the dawn of the 20th century have transitioned towards an autumn‐dominated regime, particularly in much of the central and western Spanish mainland, with these shifts possibly linked to the evolving precipitation trends. These findings contribute novel insights, particularly pre‐1950, and align with published results post‐1950 across the Mediterranean, particularly in its western regions. Furthermore, they suggest a potential connection between changes in the spatial distribution of seasonal precipitation regimes and temporal variations in the primary moisture sources over the study area.

We leveraged the most extensive and detailed gridded database of monthly precipitation data across the Spanish mainland (MOPREDAScentury), encompassing 1916–2020 time period, to pinpoint the most severe drought events within this timeframe and analyse their spatio-temporal dynamics. To identify these events, we employed the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were identified as periods of at least three months where significantly dry conditions affected 20% or more of the study area, defined as grid cells with SPI values lower than −0.84. Our analysis revealed a total of 40 major drought events. Our catalogue contains detailed information on each episode’s spatial extent, duration, severity, and spatio-temporal dynamics. The analysis of the propagation patterns of the events unveils substantial heterogeneity, implying that droughts stem from diverse atmospheric mechanisms, further influenced by complex local topography. The open-licensed drought database serves as a valuable resource. It not only facilitates exploration of drought onset and evolution mechanisms but also aids in assessing drought impact on agricultural and other socio-economic sectors.

Climate aridity (the long‐term balance between water availability through precipitation and the atmospheric evaporative demand) has a fundamental role in determining water availability and the geographic distribution of ecosystems and agricultural regions, and plays a crucial role in shaping ecological transitions under current climate change. We computed the Aridity Index, computed as the ratio of precipitation to reference evapotranspiration, over Spain for the period 1961–2020. Here we present spatially detailed climatologies of the Aridity Index, at the annual and the monthly scales, and an assessment of changes between the normal periods 1961–1990 and 1991–2020. The results show a transition towards reduced values of the Aridity Index (i.e., towards drier conditions) at the annual scale, which was more intense in the Canary Islands (where 16.3% of the territory transitioned towards more arid climate categories) than in mainland Spain and the Balearic Islands (11.6%). At the monthly level, the most striking changes over mainland Spain occurred in June, with 39.7% of the territory transitioning towards more arid categories, while transitions towards more humid conditions have only been relevant in March (23.5%) and October (13.0%) and did not compensate for the aridification trend when the whole year is considered. In the Canary Islands, the strongest changes occurred in May (22.6%) and September (19.4%), although drying trends were found almost in all months except the summer.

ABSTRACTCatalogues of past hazardous events are essential for understanding their magnitude, frequency, geographical distribution and underlying mechanisms. Such compilations provide critical data for identifying patterns, improving predictive models, and enhancing preparedness, thereby contributing to the mitigation of future risks and facilitating effective adaptation to climate‐related hazards. In this article, we present the first comprehensive catalogue of extraordinary precipitation over the Spanish mainland, spanning more than a century. This work provides valuable insights into spatial and temporal patterns of these events. We compiled monthly maxima of daily precipitation (Rx1day) exceeding 100 and 200 mm by combining the documentary collections of the AEMET National Climate Data Bank (1916–2022) with newly digitised data from the Annual Summaries of Observation Books (1916–1950). As a result, this dataset represents the most extensive and detailed collection of extraordinary precipitation events in the region. It includes 19,184 monthly maxima records above 100 mm from 4325 observatories, occurring across 4814 days (events). Additionally, there are 1130 records exceeding 200 mm, grouped into 530 days and recorded in 664 observatories. As anticipated, extraordinary rainfalls were most frequent in autumn, while summer recorded the lowest number of events. These events occurred across nearly all regions, although a higher concentration were observed near coastlines. This is particularly the case of records and events over 200 mm, which concentrated along the Mediterranean arc. We identified five spatial patterns that correspond with specific weather types. Beyond atmospheric circulation, the topography played a critical role in determining the spatial extension of extraordinary precipitations. The newly digitised dataset is available at https://digital.csic.es/handle/10261/354840, accompanied by a collection of maps of daily events. Raw data from the AEMET's National Climate Data Bank are subject to a licenced agreement.

The analysis of temporal changes in extreme event attributes, specifically magnitude and frequency, is hindered by the rarity and exceptional nature of the events being studied. The non-stationary extreme value theory (NSEVT) provides a well-established framework for assessing how extreme event probabilities vary as a function of one or more covariates. This study employs NSEVT to investigate the recent evolution and primary drivers of extreme precipitation in Spain, utilizing indices of three large-scale modes of atmospheric circulation and time as covariates. A non-stationary peaks-over-threshold model is applied to an observational network comprising 341 weather stations over the period 1951–2020. The results demonstrate that a multivariate model accounting for the influences of all covariates fits the data significantly better than simpler, univariate and stationary models in the majority of stations. The multivariate model effectively captures the spatial and temporal marginal influences of atmospheric dynamics on the magnitude-frequency relationship of different attributes of extreme precipitation events, including daily peak intensity and accumulated event precipitation. In contrast, the marginal influence of time is relatively small and sparse, lacking a spatially coherent pattern. Notably, the multivariate model reveals larger temporal influences than those inferred from the univariate model, with more stations displaying significant decreases than increases in extreme precipitation event attributes. These findings highlight the importance of considering multiple covariates and non-stationarity when analyzing temporal changes in extreme events.