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11 Pags.- 1 Tabl.- 4 Figs. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence., We present a long-term assessment of precipitation trends in Southwestern Europe (1850–2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage., This work was supported by the research projects CGL2017-82216-R, CGL2017-83866-C3-3-R and PCI2019-103631, financed by the Spanish Commission of Science and Technology and FEDER; CROSSDRO project financed by the AXIS (Assessment of Cross(X)—sectoral climate Impacts and pathways for Sustainable transformation), JPI-Climate co-funded call of the European Commission and INDECIS which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Dhais Peña-Angulo received a 'Juan de la Cierva' postdoctoral contract (FJCI-2017-33652 Spanish Ministry of Economy and Competitiveness, MEC). Conor Murphy was supported by the Irish Environmental Protection Agency (Grant Nos. 2019-CCRP-MS.60). Marco Turco has received funding from the Spanish Ministry of Science, Innovation and Universities through the project PREDFIRE (RTI2018-099711-J-I00), which is co-financed with the European Regional Development Fund (ERDF/FEDER).

The year 2020 had the most catastrophic fire season over the last two decades in the Pantanal, which led to outstanding environmental impacts. Indeed, much of the Pantanal has been affected by severe dry conditions since 2019, with evidence of the 2020’s drought being the most extreme and widespread ever recorded in the last 70 years. Although it is unquestionable that this mega-drought contributed significantly to the increase of fire risk, so far, the 2020’s fire season has been analyzed at the univariate level of a single climate event, not considering the co-occurrence of extreme and persistent temperatures with soil dryness conditions. Here, we show that similarly to other areas of the globe, the influence of land-atmosphere feedbacks contributed decisively to the simultaneous occurrence of dry and hot spells (HPs), exacerbating fire risk. The ideal synoptic conditions for strong atmospheric heating and large evaporation rates were present, in particular during the HPs, when the maximum temperature was, on average, 6 °C above the normal. The short span of the period during those compound drought-heatwave (CDHW) events accounted for 55% of the burned area of 2020. The vulnerability in the northern forested areas was higher than in the other areas, revealing a synergistic effect between fuel availability and weather-hydrological conditions. Accordingly, where fuel is not a limiting factor, fire activity tends to be more modelled by CDHW events. Our work advances beyond an isolated event-level basis towards a compound and cascading natural hazards approach, simultaneously estimating the contribution of drought and heatwaves to fuelling extreme fire outbreaks in the Pantanal such as those in 2020. Thus, these findings are relevant within a broader context, as the driving mechanisms apply across other ecosystems, implying higher flammability conditions and further efforts for monitoring and predicting such extreme events.

The Fire Weather Index (FWI) is widely used to assess the meteorological fire danger in several ecosystems worldwide. One shortcoming of the FWI is that only surface weather conditions are considered, despite the important role often played by atmospheric instability in the development of very large wildfires. In this work, we focus on the Iberian Peninsula for the period spanning 2004–2018. We show that atmospheric instability, assessed by the Continuous Haines Index (CHI), can be used to improve estimates of the probability of exceedance of energy released by fires. To achieve this, we consider a Generalized Pareto (GP) model and we show that by stepwisely introducing the FWI and then the CHI as covariates of the GP parameters, the model is improved at each stage. A comprehensive comparison of results using the GP with the FWI as a covariate and the GP with both the FWI and CHI as covariates allowed us to then define a correction to the FWI, dependent on the CHI, that we coined enhanced FWI (FWIe). Besides ensuring a better performance of this improved FWI version, it is important to stress that the proposed FWIe incorporates efficiently the effect of atmospheric instability into an estimation of fire weather danger and can be easily incorporated into existing systems.

Since 2015 the greater Cape Town area (~3.7 million people) has been experiencing the worst drought of the last century. The combined effect of this prolonged dry period with an ever-growing demand for water culminated in the widely publicized 'Day Zero' water crisis. Here we show how: (i) consecutive significant decreases in rainfall during the last three winters led to the current water crisis; (ii) the 2015–2017 record breaking drought was driven by a poleward shift of the Southern Hemisphere moisture corridor; (iii) a displacement of the jet-stream and South Atlantic storm-track has imposed significantly drier conditions to this region. Decreasing local rainfall trends are consistent with an expansion of the semi-permanent South Atlantic high pressure, and reflected in the prevalence of the positive phase of the Southern Annular Mode. Large-scale forcing mechanisms reveal the intensification and migration of subtropical anticyclones towards the mid-latitudes, highlighting the link between these circulation responses and the record warm years during 2015–2017 at the global scale.

The North Atlantic Oscillation (NAO), the East Atlantic (EA) and the Scandinavian (SCAND) modes are the three main large-scale circulation patterns driving the climate variability of the Iberian Peninsula. This study assesses their influence in terms of solar (photovoltaic) and wind power generation potential (SP and WP) and evaluates their skill as predictors. For that we use a hindcast regional climate simulation to retrieve the primary meteorological variables involved, surface solar radiation and wind speed. First we identify that the maximum influence of the various modes occurs on the interannual variations of the monthly mean SP and WP series, being generally more relevant in winter. Second we find that in this time-scale and season, SP (WP) varies up to 30% (40%) with respect to the mean climatology between years with opposite phases of the modes, although the strength and the spatial distribution of the signals differ from one month to another. Last, the skill of a multi-linear regression model (MLRM), built using the NAO, EA and SCAND indices, to reconstruct the original wintertime monthly series of SP and WP was investigated. The reconstructed series (when the MLRM is calibrated for each month individually) correlate with the original ones up to 0.8 at the interannual time-scale. Besides, when the modeled series for each individual month are merged to construct an October-to-March monthly series, and after removing the annual cycle in order to account for monthly anomalies, these correlate 0.65 (0.55) with the original SP (WP) series in average. These values remain fairly stable when the calibration and reconstruction periods differ, thus supporting up to a point the predictive potential of the method at the time-scale assessed here.