The observed trend towards warmer and drier conditions in southern Europe is projected to continue in the next decades, possibly leading to increased risk of large fires. However, an assessment of climate change impacts on fires at and above the 1.5 °C Paris target is still missing. Here, we estimate future summer burned area in Mediterranean Europe under 1.5, 2, and 3 °C global warming scenarios, accounting for possible modifications of climate-fire relationships under changed climatic conditions owing to productivity alterations. We found that such modifications could be beneficial, roughly halving the fire-intensifying signals. In any case, the burned area is robustly projected to increase. The higher the warming level is, the larger is the increase of burned area, ranging from ~40% to ~100% across the scenarios. Our results indicate that significant benefits would be obtained if warming were limited to well below 2 °C.

Future drought-hazard assessments using standardized indices depend on the period used to calibrate the probability distributions. This appears to be particularly important in a changing climate with significant trends in drought-related variables. This study explores the effect of using different approaches to project droughts, with a focus on changes in drought characteristics (frequency, duration, time spent in drought, and spatial extent), estimated with a calibration period covering recent past and future conditions (self-calibrated indices), and another one that only applies recent-past records (relative indices). The analysis focused on the Iberian Peninsula (IP), a hot-spot region where climate projections indicate significant changes by the end of this century. To do this, a EURO-CORDEX multi-model ensemble under RCP8.5 was used to calculate the Standardized Precipitation-Evapotranspiration Index (SPEI) at both 3- and 12-month timescales. The results suggest that projections of drought characteristics strongly depend on the period used to calibrate the SPEI, particularly at a 12-month timescale. Overall, differences were larger for the near future when relative indices indicated more severe droughts. For the distant future, changes were more similar, although self-calibrated indices revealed more frequent and longer-lasting droughts and the relative ones a drought worsening associated with extremely prolonged drought events.

Drought is an extreme phenomenon that will likely increase in frequency and severity in the current context of climate change. As such, it must be studied to improve the decision-making process in affected areas. As a semi-arid zone, the Guadalquivir River basin, located in the southern Iberian Peninsula, is an interesting area to perform this study. The relationship between meteorological and hydrological droughts is studied using drought indices with data from 1980 to 2012. The chosen indices are the Standardized Streamflow Index (SSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Their correlations are calculated, based on SPEI accumulation periods, and these values are analyzed with a principal component analysis to find spatial patterns in drought behavior inside the basin. This analysis was performed for the continuous series and also for monthly series, to account for seasonal changes. It has been found that the relationship of drought types occurs at different time scales depending mainly on orography and catchment area. Two main patterns were found. Generally, for low altitudes and small catchment areas, accumulation periods are shorter indicating that hydrological system in this area respond rapidly to meteorological conditions. In mountainous parts of the basin, longer accumulation periods have a stronger influence due to effects such as snowmelt.

Mountainous regions are especially vulnerable to the effects of climate change. Therefore, it is essential to study these areas, which contain an invaluable diversity of species and resources. Meanwhile, the southern part of the Iberian Peninsula (IP), as a semi-arid region, is particularly susceptible to climate change. Consequently, the mountainous area of the IP, Sierra Nevada (SN), is an area of interest that needs more research. Currently, there are different sources of meteorological, observational data for SN. There are several stations scattered across the area. However, certain difficult-to-access areas, located in the highest peaks of the IP, exhibit a lack of data. This issue can be solved by applying spatial interpolation techniques, such as the Regionalisierung der Niederschlagshöhen (RegNie) method. The result of this method is a gridded dataset for the complete region of study. Nevertheless, the coherence of the available observational data is not always guaranteed, so that the statistical analysis of the RegNie results may show low quality gridded data. There is an ongoing effort to create a curated database for the area, known as Climanevada, which is used in this research. The aim of this study is to create a gridded meteorological database using the RegNie method, containing at least precipitation and temperature data, for SN and its surrounding area. To achieve this goal, the available data for the period 1990-2020 has been deeply analyzed, searching for errors or discardable data. Once this step was complete, the most adequate grid size and resolution were chosen, considering the specific physical characteristics of the area. The whole process is described in this study, together with the description of the features of the final gridded dataset. Keywords: Sierra Nevada, precipitation, temperature, gridded data, RegNie. ACKNOWLEDGEMENTSThis research was financed by the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AIe-Services University of Granada-SierraNevada”(LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program2014-2020 (POPE), LifeWatch-ERIC action line; the project P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades; and by the project PID2021-126401OB-I00 funded by MCIN/AEI/ 10.13039/501100011033/FEDER Una manera de hacer Europa.

A large ensemble of experiments is required to reveal the predictable climate signal masked by the background noise in the decadal climate prediction (DCP). This is one of the main obstacles which complicates the generation of high‐resolution decadal climate information at regional scale, given the computing cost of the task. In this study, a set of representative sub‐ensembles of three members (ENS3) from the Decadal Prediction Large Ensemble has been selected to produce dynamically downscaled DCPs in future studies, minimizing the amount of computing resources required to conduct the regionalization while reducing as much as possible the loss of predictive skill with respect to the full ensemble (ENS40). The procedure to follow comprises two steps: first, an analysis to choose the most appropriate method of drift correction to remove the model drift; second, the selection of three members to build ENS3 and the evaluation of its performance and the impact of ensemble size on sub‐ensemble performance. The study has been focused on sea surface temperature (SST), near‐surface temperature anomaly and sea level pressure over some Coordinated Regional Climate Downscaling Experiment regions: Europe, South America and North America. The initial condition‐based approach has been shown to be the most suitable method in the three domains. Although there is an inevitable loss of predictive skill when reducing the ensemble size, ENS3 has shown to be a relatively good alternative to ENS10 and ENS40 when facing computing constraints and the analysis is focused on SST.