Analysis of synoptic weather patterns of heatwave events at regional and urban scales

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Most climate research has focused on temperature analysis to characterize HWs. We propose that a combination of temperature and synoptic patterns is a better way to define and understand HWs because including atmospheric circulation patterns provides information about different HW structures that can irregularly affect the territory, and illustrate this approach at the regional and urban scales using the Iberian peninsula and the Metropolitan Area of Barcelona as case studies. We first select HW events from 1950–2020 and apply a multivariate analysis to identify synoptic patterns based on mean sea level pressure, geopotential height at 500 hPa, and maximum daily 2 m temperature. The results indicate that four synoptic patterns reproduce at least 50% of the variance in HWs, namely, “stationary and stable”, “dynamic and advective”, “stationary and advective”, and “dynamic, advective and undulated”. Next, we apply the analysis to the Representative Concentration Pathway future scenarios (RCPs) 4.5 and 8.5 from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine how these synoptic trends can change in the future. The analysis shows that the four synoptic patterns continue to explain 55 to 60% of the variance in HWs. Future HW events will be characterized by an increase in geopotential height at 500 hPa due to the northward shift of the anticyclonic ridge. This is especially true for RCP8.5, which simulates business as usual incrementing fossil fuel use and additionally shows an increase in atmospheric dynamism in north advections from all directions in comparison with RCP4.5. These findings point to the importance of considering the geopotential height in HW prediction, as well as the direction of advections. CORDEX ERA5 reanalysis multivariate analysis climatic trends heatwaves Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 1. Introduction According to the World Meteorological Organization, the annual mean global temperature is likely to rise between 0.9–1.8°C above preindustrial levels (1850–1900) in the next five years (WMO, 2021). This global warming is mainly attributed to the increase in greenhouse gas (GHG) concentrations in the atmosphere, which mostly (78%) results from fossil fuel burning and industrial processes (Blanco et al., 2014 ). The negative effects of global warming are not equally distributed but depend on geography and weather patterns (D’ippoliti et al., 2010). According to the Mediterranean Experts on Climate and Environmental Change (MedECC), the Mediterranean region is warming 20% faster than the global average (MedECC, 2020 ). The European climate depends on mid-latitude atmospheric circulation, which is mainly controlled by westerly flows from the Atlantic Ocean (Ozturk et al., 2021 ). The Mediterranean climate is temperate with a dry summer season but suffers significant variability due to the transition between cold mild latitudes and the tropics, generating notorious circulation changes. Wind flow anomalies in the upper troposphere and the half-degree dip in the sea‒land temperature gradient may be the main causes of this atmospheric hotspot (Tuel & Eltahir, 2020 ). The Third Report on Climate Change in Catalonia (Duran et al., 2017) predicts temperature increments ranging from + 1.1 to + 2.5°C for 2031–2050 summers in comparison to 1971–2000, which is higher than the annual increment, which ranges from + 1.0 to + 2.2°C in this region. In addition to an overall temperature increase, the consequences of global warming are also expected to include an increase in the intensity and duration of heatwaves (HWs), a trend that has already been seen in recent years (Perkins-Kirkpatrick & Lewis, 2020 ). There are multiple ways to define an HW. The World Meteorological Organization (WMO, 2021) considers an HW as a five-day episode with temperatures 5°C higher than the maximum mean of May–September calculated from the reference period (1971–2000). (Peña et al., 2015 ) defined an HW as a period during which the 95th percentile of summer temperatures is reached during at least three consecutive days. Such anomalous prolonged periods of excessive heat can cause wildfires and severe negative impacts on human health, agriculture and nature (de Rigo et al., 2017; McMichael & Lindgren, 2011 ; Turco et al., 2014 ). For example, the HW of July 2003 in Europe culminated in 30,000 deaths (14,800 deaths in France) (Bouchama, 2004 ). The effects of global warming and HWs are further exacerbated in cities, which additionally suffer the urban heat island (UHI) effect due to the heat accumulation of building materials and human activity (X. Liu et al., 2018 ; Morris et al., 2017). A UHI is defined as the temperature difference between the urban center and the rural surroundings due to the property alteration of the atmospheric boundary layer (D. R. Streutker, 2002 ; Segura et al., 2021 ), including turbulence (C. S. B. Grimmond & T. R. Oke, 1999) and moisture ((Hoffmann et al., 2012). During HW episodes, UHIs can raise temperatures by 0.5°C in comparison with surrounding areas and by 2°C at night (Basara et al., 2010 ), which can lead to heat stress (Guarino et al., 2014 ; López-Bueno et al., 2021 ). Furthermore, an increase in temperature can increase the demand for energy and water for cooling, which is accompanied by an increase in pollutants (Santamouris, 2014 ). Currently, cities concentrate more than half of the world´s population, and by 2035, they are expected to hold 62.5% of the world population and 85% of the population in high-income countries (United Nations, 2020 ). As cities plan adaptive and mitigation strategies for such events (Gilabert et al., 2021 ; Segura et al., 2022 ), a rigorous understanding of potential future scenarios of HW episodes is highly relevant and necessary (United Nations, 2016). Future predictions of HW episodes state that extreme episodes such as 2003 could occur every 15 years in the 2020–2049 period (Barriopedro et al., 2011 ). Furthermore, according to (Lau & Nath, 2014 ), HWs are expected to increase in duration (by a factor of 1.4–2.0), frequency (by a factor of 2.2–4.5) and number of HW days per year (by a factor of 3–7) in Europe. To simulate and interpret future predictions of HWs, some studies have used reanalysis datasets (Bengtsson et al., 2004 ; Engdaw et al., 2021 ; Santer et al., 2004 ) together with climate models to statistically find climate trends focused on percentiles of temperature related to HWs. However, focusing solely on temperatures is insufficient to determine climate trends characterizing HWs; synoptic structures are also needed. The mechanisms that contribute to the formation of HWs do not occur independently of circulation conditions, and some configurations are more likely to produce extremely warm periods(Sfîcă et al., 2017). An analysis of the behavior of synoptic weather patterns (SWPs) could improve our understanding of HWs because including atmospheric circulation patterns provides information about the structure that generates this event. This better understanding could improve HW forecasts, which could help alert authorities and decrease the negative effects of extremely high temperatures (Della-Marta et al., 2007 ). Some studies (García-Ortega et al., 2011 ) have classified SWPs to analyze specific climate situations that generate intense events such as wildfires, but we have not found studies that use SWPs to characterize HWs. In this sense, the aim of this study is to gain knowledge about HW episodes, selecting the days using temperature standards but also differentiating these episodes by their synoptic structure. This process will clarify the different patterns that generate HWs and study the possible trends and the possible effects on future HW episodes. To define the most representative SWPs related to HWs, we classify HW episodes based on the mean sea level pressure (MSLP) and geopotential height at 500 hPa (Z500) using principal sequence pattern analysis (PSPA) (Peña et al., 2011 ), which is a variant of principal component analysis (PCA) set in T-mode (correlation between fields) instead of S-mode (correlation between temporal series). PCA has the advantages of reducing and interpreting massive datasets while simultaneously minimizing information loss. This method finds the most correlated input variables that represent the highest number of total variances possible and generates new variables, which are linear functions of those in the original dataset. (Hotelling, 1933 ; Pearson, 1901 ) utilized the first references of this method, which was not well known until computers obtained more computational power (Jolliffe et al., 2016). More recently, PCA has been applied in different fields, such as urban traffic and meteorological data (Shiva Nagendra et al., 2003) or climate change assessment (Tadić et al., 2019 ). With the overall objective of understanding HW development in terms of atmospheric structure and its effects in an urban area, we use the Metropolitan Area of Barcelona (hereafter referred to as AMB) as a case study to develop a method to classify synoptic behavior in terms of HW events. We first classify past HW events using historical ERA5 reanalysis data (1951–2020) and then analyze various simulations of future climate scenarios (2011–2100) provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine the SWPs that give rise to HW episodes. We attempt to answer two questions: (1) What are the synoptic structures that give rise to HWs? (2) How well do models forecast the synoptic structures associated with HWs? This article is organized as follows: Section 2 describes the area of study, including typical weather, land use and geography. Section 3 is dedicated to the data requirements and methods, which include the selection of HW days, the PSPA, and the creation of mean maximum temperature at 2 m (TMAX) maps in relation to SWP trends. Section 4 applies the historic analysis to future climate scenarios to analyze possible trends in the atmospheric structure. Finally, Section 5 presents a summary and conclusions. 2. Area Of Study The region of interest is the AMB, located in the northeastern Iberian Peninsula (41–42°N/1.5–2.5°E), as shown in Fig. 1. Figure 1. On the left, the domain with the surface altitude used for ERA5 and CORDEX. The Metropolitan Area of Barcelona (AMB) is marked in red. On the right, the AMB and the CORDEX pixel used for the methodology of this work are represented. The climate is Mediterranean, characterized by warm and dry summers influenced by sea breezes that regulate temperatures. According to the Fabra Weather Station (Fabra for short), the mean maximum and minimum temperatures in summer months (June, July and August: hereafter referred to as JJA) are 27.7°C and 18.8°C, respectively, with a mean summer precipitation of 96 mm (15.3% of annual total, which is 625 mm). HWs in Barcelona can reach values of + 40°C and are exacerbated by the high values of relative humidity generated by the Mediterranean Sea (summer mean of 71%), causing a situation of thermal stress for the inhabitants of Barcelona. The topography of the area is heterogeneous, with a coastal mountain range 10 km from the sea reaching + 650 m.a.s.l. (meters above sea level), two important rivers, and the delta of the Llobregat River, which covers 98 km 2 . This region covers an area of 636 km 2 and has a population of more than 3,300,000 inhabitants. From this area, 48% is urbanized, while the rest is occupied by more than 250 km 2 of green area. 3. Materials And Methods The methods employed to classify HWs into synoptic and atmospheric structures consist of the three steps outlined in Fig. 2 : (1) the classification of all the JJA days by synoptic weather types; (2) the extraction of HW days from observed data using the 95th percentile of maximum temperatures in the summer months; and (3) the application of the statistical PSPA to the HW days extracted in the previous step to reduce the dataset, improve the interpretability and find the SWP associated with HWs. In the following paragraphs, we describe each of these steps and the datasets used (all publicly available) in further detail. 3.1. Analyzing synoptic weather types and trends The JJA days from 1951–2020 are classified according to an objective synoptic classification appropriate in Mediterranean climates as described by (Miró et al., 2020 ) and shown in Table 1 . This method is based on the classic Jenkinson-Collison classification (Jenkinson & Collison, 1977 ) but with an additional geopotential level of Z500 to account for the important effect of mid-troposphere mechanisms in Mediterranean weather (Tuel & Eltahir, 2020 ; Ward, 1963 ), resulting in 13 synoptic weather types for the northeastern Iberian Peninsula. We used the Mann–Kendall test (Mann, 1945 ; Kendall, 1975 ) to classify all summer days between 1951 and 2000 into the 13 synoptic weather types. This technique has been widely used for detecting trends in hydrometeorological series (Liu et al., 2013 ; Zhang et al., 2000 ). It is a nonparametric method that is not influenced by extreme values due to its robustness to outliers in time series data. The presence of a monotonic trend in the series has been estimated using the tau statistic at the 95% level (p value 0.05). If this cannot be verified (p value < 0.05), the alternative hypothesis is accepted, which indicates an increasing or decreasing trend in the time series data. In this work, the software provided by (Hussain & Mahmud, 2019) is applied to generate the Mann–Kendall trends from the synoptic weather types. If the output value has a significant trend, the Tau-Kendall value is used to check if it is an increasing (τ > 0) or a descending trend (τ < 0). The results of these methods can be categorized into four possibilities: a nonsignificant increasing trend (NSIT), which shows an increasing trend that is not statistically significant; a nonsignificant decreasing trend (NSDT), which shows a decreasing trend that is not statistically significant; a significant increasing trend (SIT), which is statistically significant with a trend to increase; and a significant decreasing trend (SDT), which is statistically significant and has a trend to decrease. Table 1 Synoptic weather types with a brief description as defined by (Jenkinson & Collison, 1977 ) and the frequency of HW events during the period 1951–2020 in the AMB. Synoptic weather type Description % Frequency (1951–2020) Type01 West advection 0 Type02 Anticyclonic western advection 11.6 Type03 Northwest advection 0.5 Type04 North advection 1.1 Type05 Northeast advection 2.1 Type06 East advection 2.1 Type07 East advection with cutoff low above 2.7 Type08 South advection 0 Type09 Southwest advection 5.8 Type10 Trough 9 Type11 Low or cyclone 13.2 Type12 Shallow cyclone or undetermined pressure gradient 49.7 Type13 Anticyclone 2.1 3.2. Selection of heat wave episodes The HW episodes are first selected based on the definition of the Catalan Meteorological Service (SMC) adapted by (Peña et al., 2015 ), in which an HW is an episode of three or more consecutive days that reaches the 95th percentile of the maximum summer temperatures. We used hourly observational data of surface temperature available from the weather station of the Fabra Observatory (41°25’06” N, 2°07’27” E, 415 m.a.s.l.) from 1951 to 2020. This database can be downloaded at https://apidocs.meteocat.gencat.cat/ . We used data from JJA to find the HW days. The output of this step is a database of HW episodes divided into five climatic periods of 30 years each (1951–1980, 1961–1990, 1971–2000, 1981–2010, 1991–2020), following the climatological standard normal (WMO, 2016). 3.3. Applying principal sequence pattern analysis to classify heat wave episodes We refer to this third step of the methods as the PSPA. This process was applied for all days previously classified as HW days to obtain a synoptic classification related to these events. The PSPA was carried out using gridded data at a 0.25° resolution from ERA5 reanalysis (see Section 3.2.1) downloaded from the Copernicus Climate Data Store available at https://cds.climate.copernicus.eu/cdsapp#!/home from 1950 to 2020. The region used for this analysis covers from 35°N, 14°W to 50°N, 6°E, as represented in Fig. 1. Analyses of the synoptic history can be undertaken using a multivariable classification of the synoptic sequences related to the main atmospheric parameters, with an hourly or daily resolution. Methodologically, this classification is supported as a variant of PCA and is known as PSPA (Aran et al., 2011; Compagnucci et al., 2001 ; Escobar et al., 2004 ; Esteban, 2008 ; Jacobeit et al., 2006 ; Peña et al., 2015 ; Philipp, 2009 ). The analysis integrates different atmospheric levels (MSLP and Z500) with the purpose of understanding the main features that account for the dynamic atmospheric processes. The need for this has been recognized in previous studies (Houssos et al., 2008 ; Peña et al., 2015 ; Sioutas & Flocas, 2003 ), which mention the interest of using this information to build climatological classifications and implement them into forecast systems. We applied the PSPA in T-Mode to the HW dataset resulting from the previous step using a correlation matrix (Escobar et al., 2004 ; Huth et al., 2008 ), a scree test to extract the most relevant components (Cattell, 1966 ) and orthogonal Varimax rotation to satisfy the orthogonality condition of the model (Richman, 1986 ). The analysis was conducted for the domain from 30°N to 70°N and from 30°W to 30°E for the period 1951–2020. As a result, the output of the PSPA process is a set of SWPs (both from MSLP and Z500) that describe the main patterns associated with HW days. We have developed a Python script to run this and have made it publicly available through GitHub. Table 2 Data matrix used in the principal sequence pattern analysis, where x[i]j represents SLP and Z500 anomalies, varying on the n grid points ([i]) and for each of the m sequences (j). The matrix structure is in T-mode. Atm. Level Days Grid point Sequence 1 Sequence 2 … Sequence m SLP D-3 Grid [1] x [1]1 D−3 (SLP) x [1]2 D−3 (SLP) … x [1]m D−3 (SLP) … … … … … Grid [n] x [n]1 D−3 (SLP) x [n]2 D−3 (SLP) … x [n]m D−3 (SLP) … … … … … … D Grid [1] x [1]1 D−0 (SLP) x [1]2 D−0 (SLP) … x [1]m D−0 (SLP) … … … … … Grid [n] x [n]1 D−0 (SLP) x [n]2 D−0 (SLP) … x [n]m D−0 (SLP) Z500 D-3 Grid [1] x [1]1 D−6 (Z500) x [1]2 D−6 (Z500) … x [1]m D−6 (Z500) … … … … … Grid [n] x [n]1 D−3 (Z500) x [n]2 D−3 (Z500) … x [n]m D−3 (Z500) … … … … … … D Grid [1] x [1]1 D−0 (Z500) x [1]2 D−0 (Z500) … x [1]m D−0 (Z500) … … … … … Grid [n] x [n]1 D−0 (Z500) x [n]2 D−0 (Z500) … x [n]m D−0 (Z500) 3.4. Analysis of CORDEX historical HW episodes One of the main objectives of this study is to analyze how future climate change simulations predict HWs in terms of the synoptic structure. However, we first need to analyze how the climate models perform in representing synoptic behavior at the regional level. The objective of this section is to analyze the synoptic structures of the simulations available from CORDEX for the period 1951 to 2000 and determine if they are well represented when compared to reanalyzed ERA5 data. Quantile‒quantile mapping transformation (Q-Q, Amengual et al., 2012 ) is used to normalize the model simulation grid. This is done to align the statistical distribution of ERA5 reanalysis and CORDEX datasets, which removes technical variation from noisy data. This procedure consists of calculating the changes, quantile by quantile, in the cumulative distribution frequency (CDF) of the daily MSLP and Z500 outputs and the observed data. The statistical adjustment is based on the relationship between the ranked value of the corresponding CDFs for past calibrations (1951–2000), the control instrumental or baseline (1971–2000), and the raw control simulated by CORDEX models (1951–2000). The Q-Q method is applied to the CORDEX data from simulations obtained from three different regional climate models: WRF, REMO, and HIRHAM for the historical period 1951–2000 (Table 3 ). Once the model simulation outputs for MSLP and Z500 are corrected, steps 1, 2, and 3 are repeated for the period 1951 through 2000 to obtain an analysis of historical simulations (see Fig. 2 ). Table 3 Description of the CORDEX data used in this study. CMIP5 GCM EURO-CORDEX RCM Resolution ( o ) Institution (country) IPSL-CM5A-LR WRF381P 0.11 IPSL (France) MPI-M-MPI-ESM-LR REMO2009 0.11 MPI-CSC (Germany) MOHC-HadGEM2-ES HIRHAM5 0.11 DMI (Denmark) 3.5. Analysis of CORDEX future scenarios of HW episodes The same three steps 1, 2 and 3 are repeated to determine the synoptic behavior of the HW events occurring in two future scenarios for the period 2011 through 2100: representative concentration pathways (RCPs) 4.5 and 8.5. Simulations of these scenarios using the WRF, HIRHAM and REMO models are available from CORDEX. The RCPs are pollutant concentration pathways used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The values of the RCPs refer to the radiative forcing that would occur due to the increase in anthropogenic emissions by 2100 (IPCC, 2014 ). Thus, RCP4.5 is described as the most likely scenario, with greenhouse gas emissions peaking in 2040 followed by a decline. RCP8.5 is the worst-case scenario in which emissions continue to rise throughout the 21st century. The result of this step is a set of SWPs, as shown in Fig. 2 . 4. Results 4.1. Analyzing synoptic weather types and trends Applying the Mann–Kendall test to historical reanalysis data (1951–2020) shows that synoptic weather types 11 (low or cyclone) and 12 (shallow cyclone or undetermined pressure gradient) are the most frequent during the summer months, dominating up to 18% and 40% of the time, respectively, followed by types 2 (anticyclonic western advection), 5 (northeast advection) and 10 (trough), as shown in Table 4 . This means that summers are mostly characterized by undetermined pressure gradients (type 12), which are periods without any dominant high or low pressures. This pattern is commonly defined by a stationary high-pressure center located over the Azores that maintains low pressures in northern regions, blocking any possible weather front. Low pressures (type 11) can be defined by two different situations. In the first case, low pressure situations are structures with pressure levels lower than 1013 hPa, strong cyclonic winds and structures that generate weather fronts. However, in HW situations, the most common structure in the Iberian Peninsula is a thermal low resulting from the heating of the lower troposphere, generating weak cyclonic circulations. Table 4 also shows the trend determined by the Mann–Kendall method. Table 4 Percentage of times that synoptic events occur for every climatic period for JJA. Mann–Kendall (Tau-Kendall, p value and trend. NSDT: Nonsignificant decreasing trend/NSIT: Nonsignificant increasing trend/SDT: Significant decreasing trend/SIT: Significant increasing trend). *2003 not considered. 1951–1980 1961–1990 1971–2000 1981–2010* 1991–2020* Tau-Kendall p value Trend TYPE01 1.4 1.1 1.3 1.5 1.4 -0.05 0.59 NSDT TYPE02 9.6 9.7 9.8 11.1 11.1 0.05 0.58 NSIT TYPE03 2.8 2.4 2.5 2.1 2.5 0.06 0.51 NSIT TYPE04 6.2 5.1 4.9 4.4 4.9 -0.16 0.07 NSDT TYPE05 8 8.2 8.4 8.2 8.7 0.12 0.16 NSIT TYPE06 1.9 2.2 2.4 2.6 2.1 -0.05 0.56 NSDT TYPE07 2.5 2.2 1.7 1.7 1.6 -0.18 0.05 SDT TYPE08 0.04 0.1 0.1 0.1 0.04 0.03 0.81 NSIT TYPE09 3.9 3.8 4.4 3.7 3.2 -0.11 0.2 NSDT TYPE10 7.9 7.7 8.2 7.8 7.1 -0.07 0.44 NSDT TYPE11 18.1 18.6 18.6 17 16.3 -0.09 0.27 NSDT TYPE12 36.8 38 37 39 40 0.15 0.07 NSIT TYPE13 0.9 0.9 0.9 0.8 0.9 -0.05 0.61 NSDT Looking at the right of Table 4 , only one significant trend exists in the sample, which is the decreasing trend of type 07. Although type 07 has a frequency of 2.5% in the first climatic period, its trend is to decrease, and in the last climatic period, it has a frequency of 1.56%. There are two synoptic weather trends that are close to having a significant trend according to the criterion used in this work (p value ≈ 0.05), which are types 04 and 12. Type 04 has a frequency of 6.2% in the first climatic period and 4.93% in the last. With these numbers, it is close to having a significant decreasing trend. In contrast, type 12 (which is a potential HW pattern according to the synoptic classification) has an increase from 36.78–40.04%, and it is close to an increasing trend (p value = 0.07). 4.2. Selection of heat wave episodes A rising trend in temperatures from 1951 until 2020 can be noted in Fig. 3 , where each boxplot shows the mean, median and the 5th, 25th, 75th and 95th percentiles of maximum summer temperatures for the historic five time periods using observed data from the Fabra weather station. From these boxplots, the 95th percentile is extracted to select the episodes of three or more days according to the definition of HW adopted in this study. The result is a database of 139 HW days ranging from 31.9°C to 39.8°C. According to these results, the Fabra weather station registered an increment of 1.8°C for HW episodes between the first and the last climatic period under study. 4.3. Applying principal sequence pattern analysis to classify HW episodes. The PSPA method is applied for all HW days previously identified to obtain a synoptic classification related to each HW event. The results show that four SWPs are responsible for approximately 50% of the variance in MSLP and Z500 for all historic time periods, as shown in Table 4 . These four SWPs (named SWP1, SWP2, SWP3, and SWP4) are characterized by mean matrices of multiple HW days that are constructed from linear combinations of the initial variables. The four resultant SWPs are sorted by the amount of explained variance, in which SWP1 always explains more variance than SWP2 and so forth. In this study, the explained variance refers to the statistical measure that explains the variation in a dataset attributable to each of the SWPs. In the next few paragraphs, we give a description of each SWP for each climatic period, which can be found in Fig. 4 . The amount of variance can be found in the upper right of each SWP and the short-name description in the bottom left. This explained variance ratio is a metric commonly used to evaluate the utility of SWPs and to choose the number of patterns considered (Jolliffe, 2016 ). For the results, we have divided the four SWPs into four differentiated structures that are repeated in each climatic period. The different structures take into account both MSLP and Z500 variables, locating the mean elements such as low- and high-pressure centers, thermal lows, distance of the isolines or the distribution of ridges and troughs. Dynamism (main meteorological structures that change over time) and stationarity (stationary patterns that can remain permanent for multiple days) are also considered and discussed in the results. SWP-S_S: stationary and stable pattern . MSLP (Fig. 4 ) shows a thermal low and a blocking anticyclone over the Atlantic Ocean. There is a trend in surface level to an increase in the dominance of the thermal low from the north of Africa and the south of the Iberian Peninsula. The Z500 level (Fig. 5 ) provides more information about the evolution to a more stationary anticyclonic ridge due to the jet stream moving north. SWP-S_S is the first component in four out of five periods. In the 1961–1990 period is SWP2 (15.3% of the total explained variance). There is a trend toward an increase in the variance of this pattern from 15.7% in the first period to 17.1% in the last period. SWP-S_A: stationary and advective . The MSLP variable indicates a stronger high-pressure center in the western Iberian Peninsula in comparison with SWP-D_A and a thermal low in northern Africa and the Iberian Peninsula. Z500 indicates a SW flux due to the ridge in the S and the trough in the NW of the Iberian Peninsula. The variance of this pattern decreases from 14.4–10.8%. SWP-S_A is the second component in 1951–1980 (14.4% of the variance), the fourth component during the climatic period 1971–2000 (8.6% of variance), and the third component in the rest of the climatic periods (9.8%, 9.8%, and 10.8% of the variance, respectively). SWP-D_AU: dynamic, advective and undulated . MSLP indicates an area without the influence of high or low pressures with warm air masses that induce a thermal low in the Iberian Peninsula and northern Africa. There is a trend toward a decrease in high-pressure dominance in the NW. In Z500, there is an important undulation of the general circulation that advects winds from the south. There is a reduction trend for this pattern from 12.9–8.8%. SWP-D_AU is the third component in the first and third climatic periods (12.9% and 9.2% of the variance, respectively), the second component during the 1961–1990 period (15.3% of the variance), and the fourth component in the last two climatic periods (≈ 8% of the variance). SWP-D_A: dynamic and advective . MSLP shows a thermal low in the Mediterranean area due to the warm sea. The high pressures tend to lose importance due to the dominance of the thermal low. The Z500 variable shows SW flux, which is getting more undulations from the mainstream in comparison with the first climatic periods. Cold air is restricted in the Atlantic Ocean. This pattern, which is becoming more advective in height, has increased in frequency from 9.19–13.76%. SWP-D_A is the fourth component in the first climatic period (9.2%% of explained variance), the first component during the 1961–1990 period (17.1% of variance), and the third component in the rest of the climatic periods (15.5%, 14.9%, and 13.8% of the variance, respectively). Once the HW events have been classified into the four SWPs for the Iberian Peninsula, we next analyze the effect of these four SWPs on the intensity of HWs in the Metropolitan Area of Barcelona. We use a dataset providing daily temperature and precipitation from 1951 to 2020 in a regular grid of 5 km from Spain02 interpolated products (data source: https://www.aemet.es/es/serviciosclimaticos ) to generate Fig. 6 : a composite temperature map showing the mean of the daily maximum temperatures corresponding to each SWP. Days related to each SWP were selected from the Spain02 dataset (please see Supplementary Materials S1 and S2 for the tables with temperature values and S3 for the mean map covering all of Spain). In the AMB, the warmest HW patterns are SWP-D_A and SWP-D_AU, which are dynamic and advective. Specifically, SWP-D_A of the 1981–2010 period is the warmest (35.1°C, mean daily maximum temperature in the AMB), followed by SWP-S_A in 1981–2010 (35.0°C) and SWP-S_A in the 1991–2020 period (34.9°C). According to Fig. 6 , prefrontal patterns are the warmest (D_AU) in the AMB, advecting air from the S‒SW at 500 hPa. Moreover, SWP-D_A is the pattern with a thermal low over the Mediterranean, indicating a warm sea influencing the AMB temperatures. Intense ridges at 500 hPa with a thermal low at the surface (SWP-S_S) also generate warm conditions (34.6°C in the last climatic period) in the AMB but especially in the rest of the Iberian Peninsula. Maximum temperatures have increased by approximately 2°C between the first and last climatic periods analyzed. HWs registered in the AMB are mostly related to HW episodes in most of the Iberian Peninsula (see Figure S4 in the supplementary material). The only SWP that differs from the rest is SWP-D_AU, in which a cold front generates lower temperatures in the NW of the peninsula. 4.4. CORDEX historical The same analysis that was done for historical observed data (steps 1–3 of the methods section) is next repeated for the same historical period 1951–2000 JJA but with modeled CORDEX (WRF, HIRAM and REMO) data to analyze the performance of the various models. Since one of the main research objectives is to understand future HW events in terms of the SWPs in which they develop, it is important to first establish how well historical simulations of synoptic weather types are generated. First, all JJA days for the period of 1951–2000 from the output of the WRF, HIRHAM, and REMO models, as well as ERA5 reanalysis data, were classified into 13 synoptic weather types following the method described in Section 3.1 , as shown in Fig. 7 . Most JJA days of both ERA5 reanalysis and CORDEX simulations fall into synoptic weather type 12 “shallow cyclone or undetermined pressure gradient” (Miró et al., 2020 ). HIRHAM estimates more JJA days in type 12 than ERA5 (+ 5.05%), while REMO and WRF underestimate type 12 days (-9.45% and − 14.39%, respectively). The main differences among model outputs can be found in types 02, 11 and 12. REMO simulations show more dynamism at 500 hPa (due to advections from different directions) and stability at the surface level (high pressures), which generates weather types 02 and 12. HIRHAM has the most similar general structure in comparison with ERA5, with a similar percentage of cases in all the most repeated patterns (02, 11 and 12). WRF has a significant increase in synoptic weather type 11, which is related to low pressures and thermal lows. The results of the Mann–Kendall test are shown to statistically demonstrate the presence or nonpresence of trends. Table S4 represents how the CORDEX models do not simulate any significant trend in the 1951–2000 period, considering all summer days, which is consistent with the ERA5 reanalysis. Analyzing the most frequent synoptic weather type (type 12), WRF and REMO forecast a nonsignificant decrease in cases, while HIRHAM forecast a nonsignificant increase. Next, we analyze the differences among the HW days of the various models and compare them with the HW days from the observed data and the ERA5 reanalysis data. Boxplots with percentiles of temperature are generated to show the variability of every climatic period shown in Fig. 8 , where each boxplot shows the mean, median and the 5th, 25th, 75th and 95th percentiles of maximum summer temperatures. The 95th percentile, represented as the top of the whisker, is the HW temperature according to the definition used in this study. The 95th percentile of Fabra recorded an increase of + 0.81°C between 1951–1980 and 1971–2000, while ERA5 reflected an increase of + 0.55°C. CORDEX datasets register increases that range between 0.18°C (HIRHAM), 0.29°C (WRF) and 0.32°C (REMO). The adjustment of the CORDEX dataset corrects the overestimated values that all three models have, resulting in three datasets with similar percentiles in comparison with ERA5. The PSPA (3.3 of methods section) for the three CORDEX historical datasets reaches more than 50% of the variance in four SWPs (variance represented in the upper-right of the SWPs in Figs. 9 – 11 ). In this section, we show and describe the results of the WRF model due to the better representation of SWPs in comparison with ERA5. The description for all the CORDEX models is located in supplementary material S5 and REMO-HIRHAM plots in S6–S7. The criteria used to select the model that best replicates the SWPs found in ERA5 data have considered multiple factors. WRF is the model with less overestimation for the SWP1 (SWP-S_S) variance, according to Figs. 9 (MSLP), 10 (Z500) and 11 (TMAX). WRF shows a variance that ranges between 20.24 and 22.52% for SWP-S_S, which is higher than ERA5 (8.81–17.32%) but less than the rest of the CORDEX models (20.39–25.63% for REMO and 22.36–28.32% for HIRHAM). The rest of the SWPs have similar variance values (+-5%). WRF is the model that best shows the presence of the general stream undulation, especially for D_AU, in comparison with ERA5 reanalysis. This fact has been considered due to the intensity of HWs in terms of the temperature that this pattern generates, as described in the previous section. REMO and HIRHAM show the SWP-S_S with more advection at Z500 in comparison with ERA5. Due to the important amount of variance explained by this pattern (the most relevant), it is necessary to show it in a similar structure. The locations of the anticyclonic ridges (Z500) and high-pressure centers (MSLP) shown by WRF are well approximated, which is important to define the direction of wind advections to the AMB. The daily maximum temperature of each SWP is plotted for WRF in Fig. 11 (REMO and HIRHAM are found at the bottom of S6 and S7 for the Iberian Peninsula. Due to the lack of resolution (0.1°), an analysis for the AMB scale cannot be performed. The mean temperatures for the Iberian Peninsula range between 23.52–24.41°C (WRF), 22.92–24.17°C (REMO) and 22.73–23.91°C (HIRHAM), which are slightly underestimated in comparison to ERA5 (23.85–25.3°C). In all three models, the mean TMAX for the SWP-S_S is the warmest pattern in the Iberian Peninsula, which matches with ERA5. 4.5. CORDEX future scenarios Having understood how well the various models perform in terms of simulating synoptic behaviors that give rise to HW episodes, we next apply the methods described above to future scenarios RCP4.5 and RCP8.5. Figure 12 shows the classification of JJA days from the model output into the thirteen synoptic weather patterns. There is no significant difference in how the days are classified between scenarios RCP4.5 and RCP8.5 for each model. The HIRHAM model, which best estimated the synoptic weather patterns in comparison with the ERA5 reanalysis, simulates an increase in JJA days in type 12 in both scenarios, going from 42.09% in the historic CORDEX to 47.4% for RCP4.5 and 44.88% for RCP8.5. However, REMO and WRF do not simulate significant increases or decreases in type 12 with respect to ERA5. CORDEX models agree that RCP8.5 increases types 05 and 11 in comparison with RCP4.5, which are patterns with northeast advections and cyclones. On the other hand, the CORDEX models simulate a decrease in the number of days of types 06, 09 and 13 in the case of RCP8.5, which are east advections and pure anticyclones. The Mann–Kendall test was run for scenarios 4.5 (Table S8) and 8.5 (Table S9). A detailed description of all the model trends is found in supplementary material S10. Although in the CORDEX historical dataset, there are no significant trends, the CORDEX future datasets show some possible significant increasing and decreasing trends. In RCP4.5, HIRHAM is the only model that shows significant trends, which are on synoptic weather type 02 (SDT), type 12 (SIT) and type 13 (SDT). Type 12, which contains the highest number of days, shows an increasing trend from 43.37–50.57% of the JJA days. This increasing trend is not shown by WRF or CORDEX. RCP8.5 has more significant trends in all the CORDEX models. The most remarkable ones (due to the high percentage of days) are the SDT for type 02 (shown both in HIRHAM and REMO), the SIT for type 11 (shown in HIRHAM dataset) and the significant trends shown by type 12, which conflicts WRF (SDT) and REMO (SIT). Next, the temperature percentiles of both scenarios 4.5 and 8.5 are analyzed to subsequently select the HW potential days. CORDEX models show an increase in temperatures in all their percentiles (Fig. 13 ), but there are differences among them. For scenario 4.5, WRF simulations result in a small increase in the 95th percentile (+ 0.69°C) in the last climatic period (2071–2100), reaching 31.57°C in comparison to the first period (2011–2040), which reaches 30.59°C. For HIRHAM, the 95th percentile increases by + 3.17°C (from 30.53°C to 33.7°C), and REMO increases by + 0.99°C (from 30.58°C to 31.57°C). In scenario 8.5, the CORDEX models simulate a higher increase in temperatures for all percentiles. The 95th percentile for the WRF model rises 3.18°C, ranging from 30.9 to 34.08°C, HIRHAM shows an increase of 5.7°C (from 30.79 to 36.49°C) and REMO also considers an increase of 3.89°C (from 30.59 to 34.48°C). All the CORDEX models and scenarios agree in an increase in HW thresholds, which are higher in scenario 8.5. For scenario 4.5, the increase in HW thresholds by periods of 10 years ranges from + 0.12°C/10Y (WRF) to + 0.53°C/10Y (HIRHAM), while in scenario 8.5, the 95th percentile ranges from + 0.53°C/10Y (WRF) to + 0.95°C/10Y (HIRHAM). WRF is the model with the lowest heating, and HIRHAM is the model that increases the most. Next, the PSPA method described in Section 3.3 is applied to the three climatic model future simulations for both scenarios. Due to the better representation of the WRF model described in the previous section, the following description only considers this model. A detailed description of all CORDEX models is found in supplementary material S11. The WRF results are represented in Figs. 14 – 16 for the RCP4.5 scenario and Figs. 17 – 19 for the RCP8.5 scenario. To reduce the number of plots in the main description, we have chosen only three climatic periods (2011–2040, 2041–2070 and 2071–2100) to represent the whole analyzed period (2011–2100) despite the seven periods (e.g., 2011–2040, 2021–2050, …, 2071–2100). The complete sequence for WRF, HIRHAM and REMO can be found in the supplementary material (S12–S20). In general, terms, RCP4.5 shows four patterns (SWP_S-S, SWP-D_A, SWP-S_A and SWP-D_AU) in the same manner as the historical dataset. However, there are some differences and tendencies to mention. SWP-S_S: stationary and stable pattern. The variance explained by this pattern does not have important changes, remaining the most important pattern (SWP1) with a variance that ranges between 21.71 and 27.42%, while in the historical WRF dataset, it ranges between 20.24 and 22.52%. MSLP shows the same structure as the historical period with a more intense thermal low. Z500 shows the same pattern with a more intense ridge, which is moving to higher latitudes. This fact generates higher geopotential heights in the Iberian Peninsula, with maximum values that increase from 5925 m to 5975 m. SWP-D_A: dynamic and advective. The variance explained by this pattern increases from 8.82–10.85% in the historical WRF dataset (SWP4) to 12.98–17.5% in WRF4.5 (SWP2). The MSLP shows a less intense blocking anticyclone that generates a more intense thermal low, covering more terrain in southern Europe. Z500 shows a similar pattern with increased advection due to the more important gradient generated by the north movement of the anticyclonic ridge. SWP-S_A: stationary and advective. The explained variance by this pattern remains similar during WRF4.5 (9.9–11.84%) in comparison with WRF historical (10.87–11.81%). MSLP shows no differences in the general structure, although the blocking anticyclone is less intense, which generates a stronger thermal low. Z500 shows higher geopotential height values (+ 50 m in some cases) due to the north displacement of the ridge, although the structure remains the same. SWP-D_AU: dynamic, advective and undulated. The explained variance by SWP-D_AU decreases from 13.61–14.79% (WRF historical) to 6.59–9.19% (WRF4.5), which makes this pattern SWP4 (SWP2 in the historical dataset). In this sense, WRF4.5 shows less undulation in the patterns. MSLP shows a similar structure with a slightly increased blocking anticyclone. Z500 shows less advection due to the less undulated pattern and the north shift of the anticyclonic ridge. The daily maximum temperatures shown in Fig. 16 for WRF4.5 result in a significant increase with respect to historical WRF. WRF4.5 simulates the SWP-S_S pattern as the warmest on average in the Iberian Peninsula (26.31°C), followed by SWP-DA_U (25.95°C) and SWP-D_A (25.91°C), which rise 1.93°C, 2.03°C and 1.75°C, respectively, compared to the historic WRF. The PSPA has also been applied to scenario 8.5. Figures 17 (MSLP), 18 (Z500) and 19 (TMAX) show the SWPs for WRF8.5. A detailed description of WRF, HIRHAM and REMO can be found in supplementary material S21. The complete associated charts can be found in Figures S22–S30. The MSLP synoptic structure does not present significant changes in comparison with WRF4.5, although there are remarkable differences at Z500. According to WRF8.5, D_AU explains more variance (10.45–14.79%) than WRF4.5 (6.59–9.19%), and at the end of the period, it is the second most important pattern (SWP2). However, the structure of this pattern at Z500 has differences. The S‒SW advection at the Iberian Peninsula that WRF4.5 and WRF historical simulate is not observed at WRF8.5, which simulates an intense anticyclonic ridge located at the west of the Iberian Peninsula. Z500 also shows westerly flows in the other three SWPs, which differ from SW flows from WRF4.5. The maximum geopotential height values are increased, reaching 6000 m in extended parts of the Iberian Peninsula. The MSLP shows more intense and Mediterranean-centered thermal lows, which indicates the intense warming of land and sea. The temperature values for scenario 8.5 are higher in the mid and late periods in comparison with WRF4.5. The pattern that shows the highest increase is SWP-DA_U, which reaches the highest values in the last period (28.12°C in comparison to 26.26°C in WRF4.5). The SWP-S_S shows a mean temperature value in 2071–2100 of 27.81°C, which is higher in comparison to the 25.81°C simulated at WRF4.5. The rest of the patterns show less important increases. 5. Discussion 5.1. Main synoptic types associated with summer days It is important to determine the most common types of synoptic events occurring during summer days to later classify the synoptic trends of HWs. The results of the classification for the synoptic weather types applied to historical ERA5 data show that type 12 is dominant for summer days, followed by types 11 and 02. The rest of the types represent less than 10% of the summer days. Type 12 is defined as an undetermined pattern with no important advections or influence of a synoptic high- or low-pressure center (Jenkinson & Collison, 1977 ). In some cases, these patterns are reinforced by strong irradiation, which generates mesoscalar thermal low pressures in the SW of the peninsula (Hoinka & Castro, 2003 ). The intense solar radiation due to cloudy weather and the scarce wind on the surface and lower layers is a potential cause of HWs in the AMB (Gil et al., 2019 ). According to the Mann–Kendall test, this pattern shows a nonsignificant increasing trend, accounting for 37.8% of the summer days in 1951 to 40% in 2020. Types 11 and 02 are the next most frequent patterns on summer days according to ERA5, which are defined as a low or cyclone (thermal low in this case) and anticyclonic western advection, respectively. Thermal lows are generated by intense solar radiation and stability, which rises warm air from lower atmospheric layers, generating a low pressure (Portela & Castro, 1996 ), commonly produced after stationarity of type 12. However, western advections are dynamic situations that can advect warm and dry air masses from the interior of the peninsula (Mazon et al., 2014 ), generating potential HW days in the Mediterranean basin. No significant trends are found for types 11 (18.1–16.3%) or 02 (9.6–11.1%) in the 1951–2020 period. We found a significant decreasing trend for type 07 (east advection with cutoff low above), a rare synoptic type in all climatic periods (with a frequency of 2.5% in 1951–1980 to 1.56% in 1991–2020). North advections (type 04) show a nonsignificant decreasing trend that ranges from 6.2–4.93%. These results show more stability in late climate periods in comparison with the middle of the XX century, which could potentially translate to more HW potential days due to the reduction in less warm advections. The mid-1970s suffered moderate cooling (-0.03°C/year) that was followed by significant warming (+ 0.07°C/year) (Lionello & Scarascia, 2018 ). This trend is reflected in the SWPs we discuss next, in which late periods show warmer and more stable patterns than the first periods. 5.2. Main synoptic patterns associated with heatwaves at the regional and urban levels We determined that four SWPs explain more than 50% of the HW statistical variability in the climatic periods covering 1951–2020 at the AMB: two stationary patterns (SWP-S_S and SWP-S_A) that explain 27.9% of the variance and two dynamic patterns (SWP-D_A and SWP-D_AU) that explain 22.6%. The MSLP shows, in all four cases, an anticyclone over the Atlantic that blocks the possible flux of low-pressure centers or weather fronts, generating stability with long periods of cloudless conditions. Due to the latitude of the Iberian Peninsula, cloudless conditions can intensify solar radiation flux and generate positive temperature anomalies, promoting HW conditions (Tomczyk et al., 2017 ). There is also a general presence of thermal lows over the Iberian Peninsula, which are deeper in the case of intense solar radiation. The Z500 variable shows more information about general circulation, such as the presence of ridges and troughs, which could indicate warm or cold intrusions. All four SWPs show an anticyclonic ridge in the Iberian Peninsula, which, depending on its position, generates different directions and intensities of advections in height. The most stationary and static pattern associated with HWs is SWP-S_S, with a ridge that covers the entire Iberian Peninsula for at least three days. This pattern reaches a mean temperature of 34.4°C. MSLP shows a blocking anticyclone over the Atlantic and a thermal low over the peninsula. The Z500 from SWPS-S_S shows an intense ridge centered on the peninsula, which due to its stationarity can remain for long periods of time, causing a warm air mass to be generated by the peninsula itself. These synoptic conditions increase the surface sensible heat fluxes warming the air parcels near the surface adiabatically (Zschenderlein et al., 2019 ), resulting in HW events. The SWP-S_A is also stationary, although it shows a more advective pattern at Z500 with SW flux. There are two main differences between both stationary patterns (SWP-S_S and SWP-S_A). First, at MSLP, the Atlantic anticyclone is located a few km to the south in case of SWP-S_A. Second, at Z500, there is a greater geopotential height gradient due to the location of the low-pressure center in northern latitudes and the anticyclone ridge in the south. These conditions make a potential HW pattern in the E‒SE of the Iberian Peninsula, while in the NW, there are windy and colder conditions. Some studies have discussed the relation between European HWs and the tropical Atlantic conditions represented in this pattern acting as a forcing, which amplify the residence of blocking regimes and therefore increase the possibility of HWs in southern and central Europe (Cassou et al., 2005 ; Gil et al., 2019 .). The SWP-DA_U pattern is caused by a trough NW of the Iberian Peninsula that undulates the general circulation and generates advection from S‒SW. This pattern not only advects warm air from the African continent but also generates intrusions of Saharan dust (Sousa et al., 2019 ), which worsens the air quality in the Iberian Peninsula. This pattern generates the warmest HWs registered in this study due to the major south component of advection. The SWP-D_A case is characterized by the lowest surface pressures and thermal lows centered in the Mediterranean region. This is possibly generated by the temperature of the sea, which is warming 20% more than the global average (Lionello & Scarascia, 2018 ). This pattern also shows SW advection at Z500, which supports the importance of SW advections at 500 hPa in HW episodes due to warm advection. Zooming in at the urban level, the AMB is significantly influenced by the dynamic patterns SWP-D_A and SWP-DA_U (dynamic and advective and dynamic, advective and undulated, respectively). SWP-D_AU for 1981–2010 reaches a mean value of 35.1°C in the AMB, followed by SWP-D_A for 1981–2010 (35°C) and SWP-D_A for 1991–2020 (34.9°C). Cases of S‒SW advections are potentially the warmest patterns in the AMB. The mountain ranges, especially the Sistema Ibérico , which is located parallel to the Mediterranean coast, generate a foehn effect throughout the E‒NE of the Iberian Peninsula in the case of W‒SW wind flow (Peña et al., 2016 ). In that context, prefrontal patterns such as SWP-D_AU intrinsically have an SW flow that maintains warm temperatures and dry air in the AMB but not in the W‒NW of the Iberian Peninsula. Our analysis shows that there is an increasing trend of HWs in the AMB associated with SWP-S_S, characterized by an intense anticyclonic ridge that covers the Iberian Peninsula and reaches central Europe. This SWP generates a stationary pattern that can last for multiple days, covering a large part of western and central Europe. SWP-D_AU lost some frequency of HWs with respect to the beginning of the period (1951–1980), contrary to other studies (Zschenderlein et al., 2019 ). This discrepancy could be because we are explaining only 50–55% of the variance in HWs with the four SWPs and do not analyze the remaining 45–50% of the information, which we consider noise (Wold et al., 1987 ). The SWP-D_A pattern shows an increase in thermal lows in the Mediterranean regions affecting the AMB due to intense solar radiation. This fact also indicates a notable warming of the sea temperature compared to the beginning of the analyzed period, which could result in more potential HW days for the AMB. This warming of the sea coincides with (Lionello & Scarascia, 2018 ), where the Mediterranean area is considered a climate change hot spot due to the expected warming of the region compared to the rest of the planet. Our analysis of the SWPs associated with HWs in the AMB is limited due to the coarse resolution of reanalysis data, which is available at 0.25° (approx. 25 km) and is thus unable to capture human activity and building materials that exacerbate the effects of HWs (Liu et al., 2018 ; Morris et al., 2017). In this sense, different downscaling techniques are being investigated at the urban scale, albeit with a high degree of uncertainty (Duchêne et al., 2020 ; le Roy et al., 2021 ), but further work is needed to improve mesoscale and urban scale meteorological simulations. 5.3. CORDEX historical simulations: Analysis of limitations and advantages Both model simulations and reanalysis data generally agree in the summer’s overall synoptical classification, representing similar SWPs in terms of the locations of high pressures, thermal lows, ridges and troughs. However, we have found some robustness and limitations on the performance of climatic models in simulating the synoptic structure of the HW episodes that are noteworthy. The CORDEX historical simulations show discrepancies among the WRF, REMO and HIRHAM models that are worth mentioning due to the possible overestimation of HW temperatures. Regarding the TMAX variable, a positive BIAS of > 1.5°C was found in all models compared to ERA5, even after the Q-Q technique for intermodel comparison was applied. This positive BIAS agrees with what has been found in other studies such as (Lhotka et al., 2018 ), in which the 90% quantiles were calculated separately for each simulation to remove the influence of the TMAX bias. The MSLP and Z500 variables show similar patterns as ERA5, although the structures are shown in a less defined way (general decrease in geopotential height gradient) as a result of the low resolution of the models. The Z500 variable results in an overestimation of the intensity of the anticyclonic ridges, simulating values ​​that range between 5900–5925 m, while ERA5 simulates values ​​between 5800–5825 m. In this way, the three analyzed models tend to shift circulation excessively to the north. On the other hand, when analyzing the MSLP variable, the models tend to underestimate the pressure gradient, simulating more indeterminate patterns. Even so, the models are able to reflect the thermal lows and anti-cyclonic blockings in an approximate way, which contrasts with other studies where the underestimation of these blockings has been demonstrated (Scaife et al., 2010 ). Several studies have analyzed the potential limitations of HW analysis with CORDEX. The elaboration of composites can lead to multiple limitations that have been discussed in several studies (Lhotka et al., 2018 ). These limitations are related to the fact that some effects, such as advections from opposite directions, can be canceled when applying the composite of different HW days. Since HWs can be produced by different synoptic patterns, performing a composite of different days that do not have synoptic similarity between them (despite producing HWs) can be counterproductive and limiting. This is especially important for RCMs in which land‒atmosphere iteration plays a vital role in the development of HWs. In this study, this issue is largely corrected by using the PSPA, which groups the synoptic patterns for statistical proximity, separating these patterns with different synoptic structures and avoiding this "canceling out" effect. Some articles (Sfîcă et al., 2017; Zong et al., 2022 ) have remarked on the importance of the connections between extremely warm periods and synoptic structures analyzed with PCA and PSPA, such as the Western Mediterranean Oscillation found in (Mohammed et al., 2018 ). Finally, we would like to point out the limitations in analyzing HWs at the urban scale. Due to the CORDEX resolution (0.11°), it is not possible to analyze the results at the urban scale. In that sense, some articles (Duchêne et al., 2020 ; le Roy et al., 2021 ) have elaborated downscaling techniques to consider the influence of cities on the local climate (e.g., urban heat island). However, CORDEX simulations show the main HW synoptic patterns in a way that allows an analysis of the main structures of HWs and the possible future trends, as we have shown in this study. In this sense, WRF has been considered the model that best represents the synoptic structure of HWs in terms of similar variance, pressure centers and ridge locations when compared to ERA5. 5.4. Trends of future HW events based on CORDEX simulations When we analyze the HW episodes of future simulations available from CORDEX based on temperature alone, we find that the 95th percentile increases every period, resulting in warmer HW episodes. For example, HIRHAM4.5 and WRF4.5 predict an increase in the 95th percentile by + 0.12°C and + 0.53°C every ten years, respectively, for the RCP4.5 scenario. The same trend is more pronounced for RCP8.5, with a potential increment ranging from + 0.53°C/10Y for WRF8.5 to + 0.95°C/10Y for HIRHAM8.5. This increase in temperature matches the findings of the TICC report (Duran et al., 2017), which estimates an increase of 0.8°C this decade and 1.4°C by 2050 compared to 1971–2000. In that sense, both the TICCC report and the results obtained in this article point to an increase in extreme temperatures that would result in more intense HWs. Next, we determine the synoptic weather types associated with summer days of future scenarios. This step is necessary to quantify the tendency of potential HW types, such as type 12, which in ERA5 represents 40% of the summer days and 49.7% of the HW days in the 1991–2020 period. There are discrepancies between the CORDEX models for types 11 and 12. HIRHAM is the only model that shows a significant increasing trend for type 12, which would result in more potential HW days by the end of this century. However, the most notable trends are the decrease in pure anticyclones (type 13) and east advections, such as types 06 and 07, and the increase in north advections, such as types 04 and 05, indicating a more extreme climate. In that sense, all models suggest a slight tendency toward an increase in atmospheric dynamism in the summer months of JJA, generating a slightly more extreme climate than the current climate. This is especially the case for RCP8.5, where we see more advective types, such as north and northeast advections. This change in climate extremes has been found by other studies: (Wang et al., 2017 ) used Student’s test to show that climate change features an increase in warm extremes, and (Donat et al., 2016 ) confirmed that there is high confidence that temperature extremes have been warming since the middle of the twentieth century. In the mentioned works, they found a significant correlation between the changes in climate extremes and global warming. In our study, we find a similar relation, especially in RCP8.5. We see a major number of days classified as type 12 (+ 1.75%) and type 11 (+ 1.66%), which are considered potential HW types but, at the same time, an increase in cold advections from the north (+ 2.48%) in the 2011–2100 period. The PSPA for the CORDEX models gives us some insight into potential trends associated with HWs in the 2011–2100 period. The Z500 variable shows an increase in geopotential height, especially in scenario 8.5. The maximum geopotential height ranges between + 50 and + 75 m in RCP4.5 and from + 75 to + 150 m in RCP8.5. This suggests that the anticyclonic ridge will shift to the north, especially in RCP8.5, advecting warm air from northern Africa. It has been proven in multiple studies (Fischer et al., 2007 ; Serrano-Notivoli et al., 2022 ) that positive 500 hPa height anomalies are the main contributor to the increase in temperatures during HWs, which agrees with what we have found in this study, where geopotential heights are increased at the end of the century. Some articles have related the increase in extreme patterns, such as anomalous anticyclonic ridges, with the waviness of both polar and subtropical jets (Maher et al., 2020 ; Martin, 2022). According to these studies, both jets become wavier, while there are no significant trends in their average speeds. This effect generates large-scale circulation anomalies that are intrinsically linked to the HWs. Both ERA5 and CORDEX simulations show that the variable MSLP indicates an increase in thermal lows not only over the south of the peninsula, as pointed out by (Jerez et al., 2012 ) but also over the Mediterranean region, further exacerbating HW events in the AMB. 6. Conclusions This study analyzes the past and future HWs from multiple datasets: ERA5 reanalysis (historical period 1951-2020), CORDEX historical (1951-2000) and CORDEX future (RCP4.5 and RCP8.5 for the 2011–2100 period). We have evaluated the synoptical structure and significant trends associated with HW episodes to better understand the potential HW patterns and the possible evolution not only in the recent past but also in the future. The key findings can be summarized as follows: -The historical analysis (1950-2020) using ERA5 data concluded that the most dominant synoptic weather type for summer days in the Metropolitan Area of Barcelona is type 12 (undetermined pattern without influence of high or low pressures), followed by types 11 (thermal low or cyclone) and 02 (anticyclonic western advection). -The PSPA analysis based on historical ERA5 data from 1950–2020 identified some specific patterns associated with the development of HW events. Four patterns were found for each climatic period, representing at least 50% of the total HW variance. The four patterns are divided into two groups: stationary patterns (S_S and S_A) and dynamic patterns (D_A and D_AU). -The HW patterns with the highest temperatures on the AMB are the dynamic cases, with maximum mean temperatures of 35 °C. The dynamic patterns (D_A and D_AU) show advections from SW‒W that increase the temperature, especially in the Mediterranean region. However, we have found that the stationary patterns with deep anticyclonic ridges increase the temperatures in inland areas of the Iberian Peninsula. These stationary patterns can remain for multiple days over the Iberian Peninsula, generating inland warming due to intense solar radiation. -The historical CORDEX simulations, once corrected with the Q-Q technique, provide SWPs similar to those of ERA5, showing good model performance in general. This performance allows us to analyze possible future trends according to the CORDEX models for both the RCP4.5 and 8.5 scenarios. However, we found that overestimation of the geopotential height could result in overestimation of the temperatures. -The evolution of the HWs, according to the PSPA applied to CORDEX RCP4.5 and RCP8.5, has a tendency toward the intensification of the anticyclonic ridge, which reaches geopotential values higher than 6050 m. This increase in the geopotential height could generate an increase in temperatures in the lower layers of the atmosphere. -The PSPA analysis for CORDEX future simulations shows that the S‒SW advections caused by a trough in the NW of the Iberian Peninsula will be less frequent in the case of HWs at the end of the century in comparison with the present. This fact contradicts the synoptic weather type analysis, in which RCP8.5 has an increase in atmospheric dynamism. Specifically, RCP8.5 shows a decrease in anticyclones and an increase in north advections. This study has evidenced the limitations of applying RCMs at the urban scale for a more robust analysis of synoptic patterns of HWs and hopes to motivate future work to improve methods to determine the development and effect of future HWs in urban areas. Declarations Acknowledgments: This work has been made possible thanks to the financial support of the ERC Consolidator, Integrated System Analysis of Urban Vegetation and Agriculture (818002-URBAG). The authors would thank AEMET for the interpolated data from 1800 meteorological stations covering 1951-2020 provided for this work (Spain02 dataset: https://www.aemet.es/es/serviciosclimaticos/cambio_climat/datos_diarios?w=2&w2=0). We acknowledge the World Climate Research Programme's Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (listed in Table 2 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organization for Earth System Science Portals (GO-ESSP). Finally, the authors would also thank to Servei Meteorològic de Catalunya for the meteorological datasets provided by their Automatic Weather Stations. Funding: This work has been made possible thanks to the financial support of the ERC Consolidator, Integrated System Analysis of Urban Vegetation and Agriculture (818002-URBAG). Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 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Processes determining heat waves across different European climates. Quarterly Journal of the Royal Meteorological Society, 145(724), 2973-2989. Additional Declarations No competing interests reported. Supplementary Files SVSupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 19 May, 2023 Read the published version in Climate Dynamics → Version 1 posted Editorial decision: Major revision 13 Apr, 2023 Reviews received at journal 13 Apr, 2023 Reviewers agreed at journal 29 Mar, 2023 Reviews received at journal 10 Mar, 2023 Reviewers agreed at journal 05 Mar, 2023 Reviewers invited by journal 02 Mar, 2023 Editor assigned by journal 02 Mar, 2023 Submission checks completed at journal 10 Feb, 2023 First submitted to journal 01 Feb, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2539070","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":174988381,"identity":"d2a79f47-c3a8-4c96-bbc4-5cfa1cf4c5ac","order_by":0,"name":"Sergi Ventura","email":"","orcid":"","institution":"Universitat Autònoma de Barcelona (UAB)","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sergi","middleName":"","lastName":"Ventura","suffix":""},{"id":174988382,"identity":"0aafe69e-0545-4809-aa33-95b7f9ad7078","order_by":1,"name":"Josep Ramon Miró","email":"","orcid":"","institution":"Meteorological Service of Catalonia, Generalitat de Catalunya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Josep","middleName":"Ramon","lastName":"Miró","suffix":""},{"id":174988383,"identity":"888df850-59c6-4580-a1c2-85ab018426dd","order_by":2,"name":"Juan Carlos Peña","email":"","orcid":"","institution":"Meteorological Service of Catalonia, Generalitat de Catalunya","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Juan","middleName":"Carlos","lastName":"Peña","suffix":""},{"id":174988385,"identity":"4721427f-9898-451a-90a9-3bbb4c7de38d","order_by":3,"name":"Gara Villalba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIie3PMQrCMBiG4b8E4lLt+ouiV2gpaEHE0WtYhHqFDiKFQpzEC4hncFLclIBT3R0NgpNDwUUHwbS6uERHh7xjyMOXAOh0/xtWS0AYQAhg/0pMCoYkyZtsfjAvkqmvpN7di9RgnkkLW3a8z3m9CURcUwVxkoGLBpMPM/2xM1lyZx1RF1UrThQAQpL9xWdYXHJjsTFBTaZncsuJJVj5MeMdSchN+RcMKEIoCfqsUoy4LwlVrth4pl4vJ2LsVneD/jqmDS9RrUwDckjtUc2y+jtxGbbaq0J8OoSqlewJvY8jorier0RfLuh0Op0OnqVER0w/mvPqAAAAAElFTkSuQmCC","orcid":"","institution":"Universitat Autònoma de Barcelona (UAB)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Gara","middleName":"","lastName":"Villalba","suffix":""}],"badges":[],"createdAt":"2023-02-01 14:29:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2539070/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2539070/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-023-06828-1","type":"published","date":"2023-05-19T20:52:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":32876422,"identity":"f6a2402c-dff8-4467-8d3f-ccc9d8f823e1","added_by":"auto","created_at":"2023-02-13 23:24:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1020553,"visible":true,"origin":"","legend":"\u003cp\u003eOn the left, the domain with the surface altitude used for ERA5 and CORDEX. The Metropolitan Area of Barcelona (AMB) is marked in red. On the right, the AMB and the CORDEX pixel used for the methodology of this work are represented.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/9ca730ecbcbd3606acfdea40.png"},{"id":32876421,"identity":"fc8e54ec-b637-4f90-af61-6d969fd5173e","added_by":"auto","created_at":"2023-02-13 23:24:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":469496,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical workflow: Steps 1, 2, and 3 are defined at the top of the image. Rhomboids represent publicly available datasets needed to apply methods (rectangles) at each step. Triangles represent questions, and ovals include the dataset or analysis resulting from each step. The upper section (in blue) corresponds to the historical analysis based on 1951 through 2000, whereas in parallel but below and in red, the methods are applied to future (2011–2100) simulations obtained from CORDEX for future climate scenarios RCP 4.5 and 8.5.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/5b88ff160baf6594883084c1.png"},{"id":32876420,"identity":"cd8471f0-626a-4dd8-aaba-ecd411eb3fb0","added_by":"auto","created_at":"2023-02-13 23:24:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49892,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot from observations for the 1951–2020 JJA period. The boxes are defined by 25–75th quantiles, and whiskers are defined by the 5–95th quantiles. The 95th percentile is quantified (°C).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/419967dfbffd4e6d9ae2a4bf.png"},{"id":32877734,"identity":"e435893a-12e5-4ca8-b9f6-572e22f34701","added_by":"auto","created_at":"2023-02-13 23:40:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1298060,"visible":true,"origin":"","legend":"\u003cp\u003eMean sea level pressure (MSLP) variable (in hPa) for each SWP for the historical period 1951–2020. The variance % is given in the upper-right corner and the pattern in the bottom-left. Four patterns are found: S_S (stationary and stable), D_A (dynamic and advective), S_A (stationary and advective) and D_AU (dynamic, advective and undulated).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/8e88266b1f499d0a4251451e.png"},{"id":32876425,"identity":"e9133ac7-cfc1-471f-ba45-db0bc262b056","added_by":"auto","created_at":"2023-02-13 23:24:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1248738,"visible":true,"origin":"","legend":"\u003cp\u003eSWP for the ERA5 historical period. 500 hPa geopotential height (Z500) variable (in meters) plotted for 1951–2020. The variance is explained in the upper-right corner and the pattern in the bottom-left. Four patterns are found: S_S (stationary and stable), D_A (dynamic and advective), S_A (stationary and advective) and D_AU (dynamic, advective and undulated).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/d9ba543f7d0f5f79b71e9076.png"},{"id":32877885,"identity":"01dfa6ae-128a-4164-b145-6429419d087d","added_by":"auto","created_at":"2023-02-13 23:48:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":781571,"visible":true,"origin":"","legend":"\u003cp\u003eMean maximum temperature maps (in °C) for the Metropolitan Area of Barcelona associated with SWPs by climatic period: (a) 1951–1980, (b) 1961–1990 and (c) 1971–2000. Marked: Fabra Meteorological Station.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/85bbf76931def2cfa823580c.png"},{"id":32877457,"identity":"91dc04be-7d0f-43be-93aa-83ec0bf11b36","added_by":"auto","created_at":"2023-02-13 23:32:37","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":109736,"visible":true,"origin":"","legend":"\u003cp\u003eSynoptic weather types of both reanalysis (ERA5) and historical simulations (CORDEX) for the period 1951–2000. Only JJA days were selected.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/ddeb2554a8e806a4cc8628ec.png"},{"id":32877735,"identity":"561acc3f-427d-4de0-ae52-d4fef93103de","added_by":"auto","created_at":"2023-02-13 23:40:37","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":149780,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot from observations (Fabra), ERA5, and historical quantile‒quantile adjusted datasets for three periods: 1951–1980, 1961–1990 and 1971–2000. The boxes are defined by 25–75th quantiles, and whiskers are defined by the 5–95th quantiles. The 95th percentile is quantified (°C).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/a96a8c75b6891fa6cf355fd8.png"},{"id":32877460,"identity":"484becef-a67e-4eac-aef2-761085e542b0","added_by":"auto","created_at":"2023-02-13 23:32:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":908281,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX historical period. Mean sea level pressure (MSLP) variable (in hPa) plotted by the climatic periods divided by files. The columns represent the SWPs with the variance explained in the upper-right corner and the synoptic pattern in the bottom-left corner. Model: WRF\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/c2f8ad51bd9a845ea9f2875a.png"},{"id":32876432,"identity":"591153a7-9822-4141-8160-287494726148","added_by":"auto","created_at":"2023-02-13 23:24:38","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":703690,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX historical period. Geopotential height at 500 hPa (Z5000) variable (in m) plotted by the climatic periods divided by files. The columns represent the SWPs with the variance explained in the upper-right corner and the synoptic pattern in the bottom-left corner. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/fb20900c81007d0cafaa651d.png"},{"id":32876428,"identity":"6943ff7d-ab16-4c23-8e2f-432f879f6379","added_by":"auto","created_at":"2023-02-13 23:24:38","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":716937,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX historical period. Daily maximum temperature at 2 m (TMAX) variable (in °C) plotted by the climatic periods divided by files. The columns represent the SWPs with the variance explained in the upper-right corner and the synoptic pattern in the bottom-left corner. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/7db4372b13075f7366cdf403.png"},{"id":32877740,"identity":"ef9cdd46-5c39-4de4-b452-ed6c1720740e","added_by":"auto","created_at":"2023-02-13 23:40:38","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":109840,"visible":true,"origin":"","legend":"\u003cp\u003eSynoptic weather types of CORDEX (scenarios 8.5 and 4.5) for the period 1951–2000. Only JJA months were selected.\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/114c9868bb886159e912c9ff.png"},{"id":32876434,"identity":"608bafb9-9123-4e68-957c-35be523f98de","added_by":"auto","created_at":"2023-02-13 23:24:38","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":228324,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot from RCP4.5 and RCP8.5 simulations for WRF, HIRHAM and REMO JJA 2011–2100 period. The boxes are defined by the 25–75\u003csup\u003eth\u003c/sup\u003e percentile, and whiskers are defined by the 5–95th percentile. The 95th percentile is indicated at the top of the whisker.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/93a3c06f43c23a779bac901b.png"},{"id":32877884,"identity":"1d66a86c-013d-4ae5-b4d4-5048cc7a12c3","added_by":"auto","created_at":"2023-02-13 23:48:38","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":867571,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP4.5 scenario. The MSLP variable (in hPa) is represented. Resumed figure with the periods 2011–2040, 2041–2070 and 2071–2100. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/2bc326098958d99468eba6cf.png"},{"id":32876427,"identity":"2f0dda6b-fde9-4a94-ac09-24c274e9ec2e","added_by":"auto","created_at":"2023-02-13 23:24:37","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":722333,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP4.5 scenario. Geopotential height at 500 hPa (Z500) variable (in m) plotted by the climatic periods divided by files. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/1c68ce5c2179b62828e317f9.png"},{"id":32877463,"identity":"d53a2b63-f9b0-4568-9a7d-c160d11d0ec7","added_by":"auto","created_at":"2023-02-13 23:32:38","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":801310,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP4.5 scenario. Daily maximum temperature at 2 m variable (in °C) plotted by the climatic periods divided by files. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/8583d4f68789acf8d2b163e3.png"},{"id":32876438,"identity":"964b4b3d-4754-4b28-a854-ef0f984de3c1","added_by":"auto","created_at":"2023-02-13 23:24:38","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":926423,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP8.5 scenario. The MSLP variable (in hPa) is represented. Resumed figure with the periods 2011–2040, 2041–2070 and 2071–2100. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage17.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/911c0c5efd8e73ea97aaf306.png"},{"id":32877738,"identity":"0fdac454-ca94-4e44-9ce7-ee086870b56e","added_by":"auto","created_at":"2023-02-13 23:40:38","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":672507,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP8.5 scenario. Geopotential height at 500 hPa (Z5000) variable (in m) plotted by the climatic periods divided by files. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage18.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/7b02fa802f36e5a22f7e867f.png"},{"id":32877736,"identity":"749c9942-8027-4c96-8c86-187ba6ef56cc","added_by":"auto","created_at":"2023-02-13 23:40:38","extension":"png","order_by":19,"title":"Figure 19","display":"","copyAsset":false,"role":"figure","size":844000,"visible":true,"origin":"","legend":"\u003cp\u003eSWPs for the CORDEX RCP8.5 scenario. Daily maximum temperature at 2 m variable (in °C) plotted by the climatic periods divided by files. Variance explained in the upper-right corner and the pattern in the bottom-left. Model: WRF.\u003c/p\u003e","description":"","filename":"floatimage19.png","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/98fed023dea0e3302f1f83c3.png"},{"id":44729732,"identity":"38f41b18-f58a-4e66-babd-e3fffe16f8d6","added_by":"auto","created_at":"2023-10-16 21:20:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12747647,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/fef73bd3-3049-46a3-b2d8-4ce070ae04c2.pdf"},{"id":32877467,"identity":"a608cd59-fdb3-4835-b0db-9e4fe5fb06eb","added_by":"auto","created_at":"2023-02-13 23:32:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10017865,"visible":true,"origin":"","legend":"","description":"","filename":"SVSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2539070/v1/177c853a8e68a6e098b7f9e2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of synoptic weather patterns of heatwave events at regional and urban scales","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAccording to the World Meteorological Organization, the annual mean global temperature is likely to rise between 0.9\u0026ndash;1.8\u0026deg;C above preindustrial levels (1850\u0026ndash;1900) in the next five years (WMO, 2021). This global warming is mainly attributed to the increase in greenhouse gas (GHG) concentrations in the atmosphere, which mostly (78%) results from fossil fuel burning and industrial processes (Blanco et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The negative effects of global warming are not equally distributed but depend on geography and weather patterns (D\u0026rsquo;ippoliti et al., 2010). According to the Mediterranean Experts on Climate and Environmental Change (MedECC), the Mediterranean region is warming 20% faster than the global average (MedECC, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The European climate depends on mid-latitude atmospheric circulation, which is mainly controlled by westerly flows from the Atlantic Ocean (Ozturk et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The Mediterranean climate is temperate with a dry summer season but suffers significant variability due to the transition between cold mild latitudes and the tropics, generating notorious circulation changes. Wind flow anomalies in the upper troposphere and the half-degree dip in the sea‒land temperature gradient may be the main causes of this atmospheric hotspot (Tuel \u0026amp; Eltahir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Third Report on Climate Change in Catalonia (Duran et al., 2017) predicts temperature increments ranging from +\u0026thinsp;1.1 to +\u0026thinsp;2.5\u0026deg;C for 2031\u0026ndash;2050 summers in comparison to 1971\u0026ndash;2000, which is higher than the annual increment, which ranges from +\u0026thinsp;1.0 to +\u0026thinsp;2.2\u0026deg;C in this region.\u003c/p\u003e \u003cp\u003eIn addition to an overall temperature increase, the consequences of global warming are also expected to include an increase in the intensity and duration of heatwaves (HWs), a trend that has already been seen in recent years (Perkins-Kirkpatrick \u0026amp; Lewis, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There are multiple ways to define an HW. The World Meteorological Organization (WMO, 2021) considers an HW as a five-day episode with temperatures 5\u0026deg;C higher than the maximum mean of May\u0026ndash;September calculated from the reference period (1971\u0026ndash;2000). (Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) defined an HW as a period during which the 95th percentile of summer temperatures is reached during at least three consecutive days. Such anomalous prolonged periods of excessive heat can cause wildfires and severe negative impacts on human health, agriculture and nature (de Rigo et al., 2017; McMichael \u0026amp; Lindgren, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Turco et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). For example, the HW of July 2003 in Europe culminated in 30,000 deaths (14,800 deaths in France) (Bouchama, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe effects of global warming and HWs are further exacerbated in cities, which additionally suffer the urban heat island (UHI) effect due to the heat accumulation of building materials and human activity (X. Liu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morris et al., 2017). A UHI is defined as the temperature difference between the urban center and the rural surroundings due to the property alteration of the atmospheric boundary layer (D. R. Streutker, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Segura et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including turbulence (C. S. B. Grimmond \u0026amp; T. R. Oke, 1999) and moisture ((Hoffmann et al., 2012). During HW episodes, UHIs can raise temperatures by 0.5\u0026deg;C in comparison with surrounding areas and by 2\u0026deg;C at night (Basara et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which can lead to heat stress (Guarino et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; L\u0026oacute;pez-Bueno et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, an increase in temperature can increase the demand for energy and water for cooling, which is accompanied by an increase in pollutants (Santamouris, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Currently, cities concentrate more than half of the world\u0026acute;s population, and by 2035, they are expected to hold 62.5% of the world population and 85% of the population in high-income countries (United Nations, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As cities plan adaptive and mitigation strategies for such events (Gilabert et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Segura et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), a rigorous understanding of potential future scenarios of HW episodes is highly relevant and necessary (United Nations, 2016).\u003c/p\u003e \u003cp\u003eFuture predictions of HW episodes state that extreme episodes such as 2003 could occur every 15 years in the 2020\u0026ndash;2049 period (Barriopedro et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, according to (Lau \u0026amp; Nath, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), HWs are expected to increase in duration (by a factor of 1.4\u0026ndash;2.0), frequency (by a factor of 2.2\u0026ndash;4.5) and number of HW days per year (by a factor of 3\u0026ndash;7) in Europe. To simulate and interpret future predictions of HWs, some studies have used reanalysis datasets (Bengtsson et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Engdaw et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Santer et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) together with climate models to statistically find climate trends focused on percentiles of temperature related to HWs. However, focusing solely on temperatures is insufficient to determine climate trends characterizing HWs; synoptic structures are also needed. The mechanisms that contribute to the formation of HWs do not occur independently of circulation conditions, and some configurations are more likely to produce extremely warm periods(Sf\u0026icirc;că et al., 2017).\u003c/p\u003e \u003cp\u003eAn analysis of the behavior of synoptic weather patterns (SWPs) could improve our understanding of HWs because including atmospheric circulation patterns provides information about the structure that generates this event. This better understanding could improve HW forecasts, which could help alert authorities and decrease the negative effects of extremely high temperatures (Della-Marta et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Some studies (Garc\u0026iacute;a-Ortega et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have classified SWPs to analyze specific climate situations that generate intense events such as wildfires, but we have not found studies that use SWPs to characterize HWs. In this sense, the aim of this study is to gain knowledge about HW episodes, selecting the days using temperature standards but also differentiating these episodes by their synoptic structure. This process will clarify the different patterns that generate HWs and study the possible trends and the possible effects on future HW episodes.\u003c/p\u003e \u003cp\u003eTo define the most representative SWPs related to HWs, we classify HW episodes based on the mean sea level pressure (MSLP) and geopotential height at 500 hPa (Z500) using principal sequence pattern analysis (PSPA) (Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which is a variant of principal component analysis (PCA) set in T-mode (correlation between fields) instead of S-mode (correlation between temporal series). PCA has the advantages of reducing and interpreting massive datasets while simultaneously minimizing information loss. This method finds the most correlated input variables that represent the highest number of total variances possible and generates new variables, which are linear functions of those in the original dataset. (Hotelling, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1933\u003c/span\u003e; Pearson, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1901\u003c/span\u003e) utilized the first references of this method, which was not well known until computers obtained more computational power (Jolliffe et al., 2016). More recently, PCA has been applied in different fields, such as urban traffic and meteorological data (Shiva Nagendra et al., 2003) or climate change assessment (Tadić et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWith the overall objective of understanding HW development in terms of atmospheric structure and its effects in an urban area, we use the Metropolitan Area of Barcelona (hereafter referred to as AMB) as a case study to develop a method to classify synoptic behavior in terms of HW events. We first classify past HW events using historical ERA5 reanalysis data (1951\u0026ndash;2020) and then analyze various simulations of future climate scenarios (2011\u0026ndash;2100) provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine the SWPs that give rise to HW episodes. We attempt to answer two questions: (1) What are the synoptic structures that give rise to HWs? (2) How well do models forecast the synoptic structures associated with HWs?\u003c/p\u003e \u003cp\u003eThis article is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e describes the area of study, including typical weather, land use and geography. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e is dedicated to the data requirements and methods, which include the selection of HW days, the PSPA, and the creation of mean maximum temperature at 2 m (TMAX) maps in relation to SWP trends. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e applies the historic analysis to future climate scenarios to analyze possible trends in the atmospheric structure. Finally, Section \u003cspan refid=\"Sec15\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents a summary and conclusions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Area Of Study","content":"\u003cp\u003eThe region of interest is the AMB, located in the northeastern Iberian Peninsula (41\u0026ndash;42\u0026deg;N/1.5\u0026ndash;2.5\u0026deg;E), as shown in Fig.\u0026nbsp;1. \u003cstrong\u003eFigure 1.\u003c/strong\u003e On the left, the domain with the surface altitude used for ERA5 and CORDEX. The Metropolitan Area of Barcelona (AMB) is marked in red. On the right, the AMB and the CORDEX pixel used for the methodology of this work are represented.\u003c/p\u003e\n\u003cp\u003eThe climate is Mediterranean, characterized by warm and dry summers influenced by sea breezes that regulate temperatures. According to the Fabra Weather Station (Fabra for short), the mean maximum and minimum temperatures in summer months (June, July and August: hereafter referred to as JJA) are 27.7\u0026deg;C and 18.8\u0026deg;C, respectively, with a mean summer precipitation of 96 mm (15.3% of annual total, which is 625 mm). HWs in Barcelona can reach values of +\u0026thinsp;40\u0026deg;C and are exacerbated by the high values of relative humidity generated by the Mediterranean Sea (summer mean of 71%), causing a situation of thermal stress for the inhabitants of Barcelona.\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe topography of the area is heterogeneous, with a coastal mountain range 10 km from the sea reaching\u0026thinsp;+\u0026thinsp;650 m.a.s.l. (meters above sea level), two important rivers, and the delta of the Llobregat River, which covers 98 km\u003csup\u003e2\u003c/sup\u003e. This region covers an area of 636 km\u003csup\u003e2\u003c/sup\u003e and has a population of more than 3,300,000 inhabitants. From this area, 48% is urbanized, while the rest is occupied by more than 250 km\u003csup\u003e2\u003c/sup\u003e of green area.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Materials And Methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe methods employed to classify HWs into synoptic and atmospheric structures consist of the three steps outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e: (1) the classification of all the JJA days by synoptic weather types; (2) the extraction of HW days from observed data using the 95th percentile of maximum temperatures in the summer months; and (3) the application of the statistical PSPA to the HW days extracted in the previous step to reduce the dataset, improve the interpretability and find the SWP associated with HWs. In the following paragraphs, we describe each of these steps and the datasets used (all publicly available) in further detail.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Analyzing synoptic weather types and trends\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe JJA days from 1951\u0026ndash;2020 are classified according to an objective synoptic classification appropriate in Mediterranean climates as described by (Mir\u0026oacute; et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This method is based on the classic Jenkinson-Collison classification (Jenkinson \u0026amp; Collison, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) but with an additional geopotential level of Z500 to account for the important effect of mid-troposphere mechanisms in Mediterranean weather (Tuel \u0026amp; Eltahir, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ward, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e1963\u003c/span\u003e), resulting in 13 synoptic weather types for the northeastern Iberian Peninsula.\u003c/p\u003e \u003cp\u003eWe used the Mann\u0026ndash;Kendall test (Mann, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) to classify all summer days between 1951 and 2000 into the 13 synoptic weather types. This technique has been widely used for detecting trends in hydrometeorological series (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). It is a nonparametric method that is not influenced by extreme values due to its robustness to outliers in time series data. The presence of a monotonic trend in the series has been estimated using the tau statistic at the 95% level (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Mann\u0026ndash;Kendall test checks the null hypothesis, which indicates that there is no significant trend (p value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). If this cannot be verified (p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), the alternative hypothesis is accepted, which indicates an increasing or decreasing trend in the time series data. In this work, the software provided by (Hussain \u0026amp; Mahmud, 2019) is applied to generate the Mann\u0026ndash;Kendall trends from the synoptic weather types. If the output value has a significant trend, the Tau-Kendall value is used to check if it is an increasing (τ\u0026thinsp;\u0026gt;\u0026thinsp;0) or a descending trend (τ\u0026thinsp;\u0026lt;\u0026thinsp;0). The results of these methods can be categorized into four possibilities: a nonsignificant increasing trend (NSIT), which shows an increasing trend that is not statistically significant; a nonsignificant decreasing trend (NSDT), which shows a decreasing trend that is not statistically significant; a significant increasing trend (SIT), which is statistically significant with a trend to increase; and a significant decreasing trend (SDT), which is statistically significant and has a trend to decrease.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSynoptic weather types with a brief description as defined by (Jenkinson \u0026amp; Collison, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1977\u003c/span\u003e) and the frequency of HW events during the period 1951\u0026ndash;2020 in the AMB.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynoptic weather type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% Frequency (1951\u0026ndash;2020)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWest advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnticyclonic western advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorthwest advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorth advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNortheast advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType06\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEast advection with cutoff low above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest advection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow or cyclone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShallow cyclone or undetermined pressure gradient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnticyclone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Selection of heat wave episodes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe HW episodes are first selected based on the definition of the Catalan Meteorological Service (SMC) adapted by (Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), in which an HW is an episode of three or more consecutive days that reaches the 95th percentile of the maximum summer temperatures.\u003c/p\u003e \u003cp\u003eWe used hourly observational data of surface temperature available from the weather station of the Fabra Observatory (41\u0026deg;25\u0026rsquo;06\u0026rdquo; N, 2\u0026deg;07\u0026rsquo;27\u0026rdquo; E, 415 m.a.s.l.) from 1951 to 2020. This database can be downloaded at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apidocs.meteocat.gencat.cat/\u003c/span\u003e\u003cspan address=\"https://apidocs.meteocat.gencat.cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. We used data from JJA to find the HW days. The output of this step is a database of HW episodes divided into five climatic periods of 30 years each (1951\u0026ndash;1980, 1961\u0026ndash;1990, 1971\u0026ndash;2000, 1981\u0026ndash;2010, 1991\u0026ndash;2020), following the climatological standard normal (WMO, 2016).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Applying principal sequence pattern analysis to classify heat wave episodes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe refer to this third step of the methods as the PSPA. This process was applied for all days previously classified as HW days to obtain a synoptic classification related to these events. The PSPA was carried out using gridded data at a 0.25\u0026deg; resolution from ERA5 reanalysis (see Section 3.2.1) downloaded from the Copernicus Climate Data Store available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/home\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e from 1950 to 2020. The region used for this analysis covers from 35\u0026deg;N, 14\u0026deg;W to 50\u0026deg;N, 6\u0026deg;E, as represented in Fig.\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eAnalyses of the synoptic history can be undertaken using a multivariable classification of the synoptic sequences related to the main atmospheric parameters, with an hourly or daily resolution. Methodologically, this classification is supported as a variant of PCA and is known as PSPA (Aran et al., 2011; Compagnucci et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Escobar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Esteban, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Jacobeit et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Philipp, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The analysis integrates different atmospheric levels (MSLP and Z500) with the purpose of understanding the main features that account for the dynamic atmospheric processes. The need for this has been recognized in previous studies (Houssos et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Sioutas \u0026amp; Flocas, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), which mention the interest of using this information to build climatological classifications and implement them into forecast systems.\u003c/p\u003e \u003cp\u003eWe applied the PSPA in T-Mode to the HW dataset resulting from the previous step using a correlation matrix (Escobar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Huth et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a scree test to extract the most relevant components (Cattell, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) and orthogonal Varimax rotation to satisfy the orthogonality condition of the model (Richman, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). The analysis was conducted for the domain from 30\u0026deg;N to 70\u0026deg;N and from 30\u0026deg;W to 30\u0026deg;E for the period 1951\u0026ndash;2020. As a result, the output of the PSPA process is a set of SWPs (both from MSLP and Z500) that describe the main patterns associated with HW days. We have developed a Python script to run this and have made it publicly available through GitHub.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eData matrix used in the principal sequence pattern analysis, where x[i]j represents SLP and Z500 anomalies, varying on the n grid points ([i]) and for each of the m sequences (j). The matrix structure is in T-mode.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtm. Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDays\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrid point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSequence 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSequence 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026hellip;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSequence m\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eSLP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eD-3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid [1]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e 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colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(SLP)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid [1]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;6\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;6\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;6\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid [n]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;3\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid [1]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[1]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGrid [n]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]1\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]2\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026hellip;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ex\u003c/b\u003e\u003csub\u003e\u003cb\u003e[n]m\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003eD\u0026minus;0\u003c/b\u003e\u003c/sup\u003e(Z500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Analysis of CORDEX historical HW episodes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eOne of the main objectives of this study is to analyze how future climate change simulations predict HWs in terms of the synoptic structure. However, we first need to analyze how the climate models perform in representing synoptic behavior at the regional level. The objective of this section is to analyze the synoptic structures of the simulations available from CORDEX for the period 1951 to 2000 and determine if they are well represented when compared to reanalyzed ERA5 data.\u003c/p\u003e \u003cp\u003eQuantile‒quantile mapping transformation (Q-Q, Amengual et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) is used to normalize the model simulation grid. This is done to align the statistical distribution of ERA5 reanalysis and CORDEX datasets, which removes technical variation from noisy data. This procedure consists of calculating the changes, quantile by quantile, in the cumulative distribution frequency (CDF) of the daily MSLP and Z500 outputs and the observed data. The statistical adjustment is based on the relationship between the ranked value of the corresponding CDFs for past calibrations (1951\u0026ndash;2000), the control instrumental or baseline (1971\u0026ndash;2000), and the raw control simulated by CORDEX models (1951\u0026ndash;2000). The Q-Q method is applied to the CORDEX data from simulations obtained from three different regional climate models: WRF, REMO, and HIRHAM for the historical period 1951\u0026ndash;2000 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Once the model simulation outputs for MSLP and Z500 are corrected, steps 1, 2, and 3 are repeated for the period 1951 through 2000 to obtain an analysis of historical simulations (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the CORDEX data used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMIP5 GCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEURO-CORDEX RCM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResolution (\u003csup\u003eo\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInstitution (country)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPSL-CM5A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWRF381P\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPSL (France)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-M-MPI-ESM-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eREMO2009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMPI-CSC (Germany)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOHC-HadGEM2-ES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHIRHAM5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDMI (Denmark)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Analysis of CORDEX future scenarios of HW episodes\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe same three steps 1, 2 and 3 are repeated to determine the synoptic behavior of the HW events occurring in two future scenarios for the period 2011 through 2100: representative concentration pathways (RCPs) 4.5 and 8.5. Simulations of these scenarios using the WRF, HIRHAM and REMO models are available from CORDEX. The RCPs are pollutant concentration pathways used in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The values of the RCPs refer to the radiative forcing that would occur due to the increase in anthropogenic emissions by 2100 (IPCC, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Thus, RCP4.5 is described as the most likely scenario, with greenhouse gas emissions peaking in 2040 followed by a decline. RCP8.5 is the worst-case scenario in which emissions continue to rise throughout the 21st century. The result of this step is a set of SWPs, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003ch2\u003e4.1. Analyzing synoptic weather types and trends\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eApplying the Mann\u0026ndash;Kendall test to historical reanalysis data (1951\u0026ndash;2020) shows that synoptic weather types 11 (low or cyclone) and 12 (shallow cyclone or undetermined pressure gradient) are the most frequent during the summer months, dominating up to 18% and 40% of the time, respectively, followed by types 2 (anticyclonic western advection), 5 (northeast advection) and 10 (trough), as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. This means that summers are mostly characterized by undetermined pressure gradients (type 12), which are periods without any dominant high or low pressures. This pattern is commonly defined by a stationary high-pressure center located over the Azores that maintains low pressures in northern regions, blocking any possible weather front. Low pressures (type 11) can be defined by two different situations. In the first case, low pressure situations are structures with pressure levels lower than 1013 hPa, strong cyclonic winds and structures that generate weather fronts. However, in HW situations, the most common structure in the Iberian Peninsula is a thermal low resulting from the heating of the lower troposphere, generating weak cyclonic circulations. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e also shows the trend determined by the Mann\u0026ndash;Kendall method.\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003eTable 4\u0026nbsp;\u003cp\u003ePercentage of times that synoptic events occur for every climatic period for JJA. Mann\u0026ndash;Kendall (Tau-Kendall, p value and trend. NSDT: Nonsignificant decreasing trend/NSIT: Nonsignificant increasing trend/SDT: Significant decreasing trend/SIT: Significant increasing trend). *2003 not considered.\u003c/p\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth\u003e\u003cbr\u003e\u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003e1951\u0026ndash;1980\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003e1961\u0026ndash;1990\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003e1971\u0026ndash;2000\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003e1981\u0026ndash;2010*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003e1991\u0026ndash;2020*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003eTau-Kendall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth\u003e\n \u003cp\u003eTrend\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSIT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n 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\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSIT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE07\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.05\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSDT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE08\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSIT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSIT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTYPE13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNSDT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eLooking at the right of Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, only one significant trend exists in the sample, which is the decreasing trend of type 07. Although type 07 has a frequency of 2.5% in the first climatic period, its trend is to decrease, and in the last climatic period, it has a frequency of 1.56%.\u003c/p\u003e\n\u003cp\u003eThere are two synoptic weather trends that are close to having a significant trend according to the criterion used in this work (p value\u0026thinsp;\u0026asymp;\u0026thinsp;0.05), which are types 04 and 12. Type 04 has a frequency of 6.2% in the first climatic period and 4.93% in the last. With these numbers, it is close to having a significant decreasing trend. In contrast, type 12 (which is a potential HW pattern according to the synoptic classification) has an increase from 36.78\u0026ndash;40.04%, and it is close to an increasing trend (p value\u0026thinsp;=\u0026thinsp;0.07).\u003c/p\u003e\n\u003ch2\u003e4.2. Selection of heat wave episodes\u003c/h2\u003e\n\u003cp\u003eA rising trend in temperatures from 1951 until 2020 can be noted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, where each boxplot shows the mean, median and the 5th, 25th, 75th and 95th percentiles of maximum summer temperatures for the historic five time periods using observed data from the Fabra weather station. From these boxplots, the 95th percentile is extracted to select the episodes of three or more days according to the definition of HW adopted in this study. The result is a database of 139 HW days ranging from 31.9\u0026deg;C to 39.8\u0026deg;C. According to these results, the Fabra weather station registered an increment of 1.8\u0026deg;C for HW episodes between the first and the last climatic period under study.\u003c/p\u003e\n\u003ch2\u003e4.3. Applying principal sequence pattern analysis to classify HW episodes.\u003c/h2\u003e\n\u003cp\u003eThe PSPA method is applied for all HW days previously identified to obtain a synoptic classification related to each HW event. The results show that four SWPs are responsible for approximately 50% of the variance in MSLP and Z500 for all historic time periods, as shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. These four SWPs (named SWP1, SWP2, SWP3, and SWP4) are characterized by mean matrices of multiple HW days that are constructed from linear combinations of the initial variables. The four resultant SWPs are sorted by the amount of explained variance, in which SWP1 always explains more variance than SWP2 and so forth. In this study, the explained variance refers to the statistical measure that explains the variation in a dataset attributable to each of the SWPs.\u003c/p\u003e\n\u003cp\u003eIn the next few paragraphs, we give a description of each SWP for each climatic period, which can be found in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The amount of variance can be found in the upper right of each SWP and the short-name description in the bottom left. This explained variance ratio is a metric commonly used to evaluate the utility of SWPs and to choose the number of patterns considered (Jolliffe, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). For the results, we have divided the four SWPs into four differentiated structures that are repeated in each climatic period. The different structures take into account both MSLP and Z500 variables, locating the mean elements such as low- and high-pressure centers, thermal lows, distance of the isolines or the distribution of ridges and troughs. Dynamism (main meteorological structures that change over time) and stationarity (stationary patterns that can remain permanent for multiple days) are also considered and discussed in the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-S_S: stationary and stable pattern\u003c/strong\u003e. MSLP (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) shows a thermal low and a blocking anticyclone over the Atlantic Ocean. There is a trend in surface level to an increase in the dominance of the thermal low from the north of Africa and the south of the Iberian Peninsula. The Z500 level (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) provides more information about the evolution to a more stationary anticyclonic ridge due to the jet stream moving north. SWP-S_S is the first component in four out of five periods. In the 1961\u0026ndash;1990 period is SWP2 (15.3% of the total explained variance). There is a trend toward an increase in the variance of this pattern from 15.7% in the first period to 17.1% in the last period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-S_A: stationary and advective\u003c/strong\u003e. The MSLP variable indicates a stronger high-pressure center in the western Iberian Peninsula in comparison with SWP-D_A and a thermal low in northern Africa and the Iberian Peninsula. Z500 indicates a SW flux due to the ridge in the S and the trough in the NW of the Iberian Peninsula. The variance of this pattern decreases from 14.4\u0026ndash;10.8%. SWP-S_A is the second component in 1951\u0026ndash;1980 (14.4% of the variance), the fourth component during the climatic period 1971\u0026ndash;2000 (8.6% of variance), and the third component in the rest of the climatic periods (9.8%, 9.8%, and 10.8% of the variance, respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-D_AU: dynamic, advective and undulated\u003c/strong\u003e. MSLP indicates an area without the influence of high or low pressures with warm air masses that induce a thermal low in the Iberian Peninsula and northern Africa. There is a trend toward a decrease in high-pressure dominance in the NW. In Z500, there is an important undulation of the general circulation that advects winds from the south. There is a reduction trend for this pattern from 12.9\u0026ndash;8.8%. SWP-D_AU is the third component in the first and third climatic periods (12.9% and 9.2% of the variance, respectively), the second component during the 1961\u0026ndash;1990 period (15.3% of the variance), and the fourth component in the last two climatic periods (\u0026asymp;\u0026thinsp;8% of the variance).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-D_A: dynamic and advective\u003c/strong\u003e. MSLP shows a thermal low in the Mediterranean area due to the warm sea. The high pressures tend to lose importance due to the dominance of the thermal low. The Z500 variable shows SW flux, which is getting more undulations from the mainstream in comparison with the first climatic periods. Cold air is restricted in the Atlantic Ocean. This pattern, which is becoming more advective in height, has increased in frequency from 9.19\u0026ndash;13.76%. SWP-D_A is the fourth component in the first climatic period (9.2%% of explained variance), the first component during the 1961\u0026ndash;1990 period (17.1% of variance), and the third component in the rest of the climatic periods (15.5%, 14.9%, and 13.8% of the variance, respectively).\u003c/p\u003e\n\u003cp\u003eOnce the HW events have been classified into the four SWPs for the Iberian Peninsula, we next analyze the effect of these four SWPs on the intensity of HWs in the Metropolitan Area of Barcelona. We use a dataset providing daily temperature and precipitation from 1951 to 2020 in a regular grid of 5 km from Spain02 interpolated products (data source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.aemet.es/es/serviciosclimaticos\u003c/span\u003e\u003c/span\u003e) to generate Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e: a composite temperature map showing the mean of the daily maximum temperatures corresponding to each SWP. Days related to each SWP were selected from the Spain02 dataset (please see Supplementary Materials S1 and S2 for the tables with temperature values and S3 for the mean map covering all of Spain).\u003c/p\u003e\n\u003cp\u003eIn the AMB, the warmest HW patterns are SWP-D_A and SWP-D_AU, which are dynamic and advective. Specifically, SWP-D_A of the 1981\u0026ndash;2010 period is the warmest (35.1\u0026deg;C, mean daily maximum temperature in the AMB), followed by SWP-S_A in 1981\u0026ndash;2010 (35.0\u0026deg;C) and SWP-S_A in the 1991\u0026ndash;2020 period (34.9\u0026deg;C). According to Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, prefrontal patterns are the warmest (D_AU) in the AMB, advecting air from the S‒SW at 500 hPa. Moreover, SWP-D_A is the pattern with a thermal low over the Mediterranean, indicating a warm sea influencing the AMB temperatures. Intense ridges at 500 hPa with a thermal low at the surface (SWP-S_S) also generate warm conditions (34.6\u0026deg;C in the last climatic period) in the AMB but especially in the rest of the Iberian Peninsula. Maximum temperatures have increased by approximately 2\u0026deg;C between the first and last climatic periods analyzed.\u003c/p\u003e\n\u003cp\u003eHWs registered in the AMB are mostly related to HW episodes in most of the Iberian Peninsula (see Figure S4 in the supplementary material). The only SWP that differs from the rest is SWP-D_AU, in which a cold front generates lower temperatures in the NW of the peninsula.\u003c/p\u003e\n\u003ch2\u003e4.4. CORDEX historical\u003c/h2\u003e\n\u003cp\u003eThe same analysis that was done for historical observed data (steps 1\u0026ndash;3 of the methods section) is next repeated for the same historical period 1951\u0026ndash;2000 JJA but with modeled CORDEX (WRF, HIRAM and REMO) data to analyze the performance of the various models. Since one of the main research objectives is to understand future HW events in terms of the SWPs in which they develop, it is important to first establish how well historical simulations of synoptic weather types are generated. First, all JJA days for the period of 1951\u0026ndash;2000 from the output of the WRF, HIRHAM, and REMO models, as well as ERA5 reanalysis data, were classified into 13 synoptic weather types following the method described in Section \u003cspan class=\"InternalRef\"\u003e3.1\u003c/span\u003e, as shown in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Most JJA days of both ERA5 reanalysis and CORDEX simulations fall into synoptic weather type 12 \u0026ldquo;shallow cyclone or undetermined pressure gradient\u0026rdquo; (Mir\u0026oacute; et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). HIRHAM estimates more JJA days in type 12 than ERA5 (+\u0026thinsp;5.05%), while REMO and WRF underestimate type 12 days (-9.45% and \u0026minus;\u0026thinsp;14.39%, respectively).\u003c/p\u003e\n\u003cp\u003eThe main differences among model outputs can be found in types 02, 11 and 12. REMO simulations show more dynamism at 500 hPa (due to advections from different directions) and stability at the surface level (high pressures), which generates weather types 02 and 12. HIRHAM has the most similar general structure in comparison with ERA5, with a similar percentage of cases in all the most repeated patterns (02, 11 and 12). WRF has a significant increase in synoptic weather type 11, which is related to low pressures and thermal lows.\u003c/p\u003e\n\u003cp\u003eThe results of the Mann\u0026ndash;Kendall test are shown to statistically demonstrate the presence or nonpresence of trends. Table S4 represents how the CORDEX models do not simulate any significant trend in the 1951\u0026ndash;2000 period, considering all summer days, which is consistent with the ERA5 reanalysis. Analyzing the most frequent synoptic weather type (type 12), WRF and REMO forecast a nonsignificant decrease in cases, while HIRHAM forecast a nonsignificant increase.\u003c/p\u003e\n\u003cp\u003eNext, we analyze the differences among the HW days of the various models and compare them with the HW days from the observed data and the ERA5 reanalysis data. Boxplots with percentiles of temperature are generated to show the variability of every climatic period shown in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, where each boxplot shows the mean, median and the 5th, 25th, 75th and 95th percentiles of maximum summer temperatures. The 95th percentile, represented as the top of the whisker, is the HW temperature according to the definition used in this study.\u003c/p\u003e\n\u003cp\u003eThe 95th percentile of Fabra recorded an increase of +\u0026thinsp;0.81\u0026deg;C between 1951\u0026ndash;1980 and 1971\u0026ndash;2000, while ERA5 reflected an increase of +\u0026thinsp;0.55\u0026deg;C. CORDEX datasets register increases that range between 0.18\u0026deg;C (HIRHAM), 0.29\u0026deg;C (WRF) and 0.32\u0026deg;C (REMO). The adjustment of the CORDEX dataset corrects the overestimated values that all three models have, resulting in three datasets with similar percentiles in comparison with ERA5.\u003c/p\u003e\n\u003cp\u003eThe PSPA (3.3 of methods section) for the three CORDEX historical datasets reaches more than 50% of the variance in four SWPs (variance represented in the upper-right of the SWPs in Figs. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e). In this section, we show and describe the results of the WRF model due to the better representation of SWPs in comparison with ERA5. The description for all the CORDEX models is located in supplementary material S5 and REMO-HIRHAM plots in S6\u0026ndash;S7.\u003c/p\u003e\n\u003cp\u003eThe criteria used to select the model that best replicates the SWPs found in ERA5 data have considered multiple factors. WRF is the model with less overestimation for the SWP1 (SWP-S_S) variance, according to Figs. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e (MSLP), 10 (Z500) and 11 (TMAX). WRF shows a variance that ranges between 20.24 and 22.52% for SWP-S_S, which is higher than ERA5 (8.81\u0026ndash;17.32%) but less than the rest of the CORDEX models (20.39\u0026ndash;25.63% for REMO and 22.36\u0026ndash;28.32% for HIRHAM). The rest of the SWPs have similar variance values (+-5%). WRF is the model that best shows the presence of the general stream undulation, especially for D_AU, in comparison with ERA5 reanalysis. This fact has been considered due to the intensity of HWs in terms of the temperature that this pattern generates, as described in the previous section. REMO and HIRHAM show the SWP-S_S with more advection at Z500 in comparison with ERA5. Due to the important amount of variance explained by this pattern (the most relevant), it is necessary to show it in a similar structure. The locations of the anticyclonic ridges (Z500) and high-pressure centers (MSLP) shown by WRF are well approximated, which is important to define the direction of wind advections to the AMB.\u003c/p\u003e\n\u003cp\u003eThe daily maximum temperature of each SWP is plotted for WRF in Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e (REMO and HIRHAM are found at the bottom of S6 and S7 for the Iberian Peninsula. Due to the lack of resolution (0.1\u0026deg;), an analysis for the AMB scale cannot be performed. The mean temperatures for the Iberian Peninsula range between 23.52\u0026ndash;24.41\u0026deg;C (WRF), 22.92\u0026ndash;24.17\u0026deg;C (REMO) and 22.73\u0026ndash;23.91\u0026deg;C (HIRHAM), which are slightly underestimated in comparison to ERA5 (23.85\u0026ndash;25.3\u0026deg;C). In all three models, the mean TMAX for the SWP-S_S is the warmest pattern in the Iberian Peninsula, which matches with ERA5.\u003c/p\u003e\n\u003ch2\u003e4.5. CORDEX future scenarios\u003c/h2\u003e\n\u003cp\u003eHaving understood how well the various models perform in terms of simulating synoptic behaviors that give rise to HW episodes, we next apply the methods described above to future scenarios RCP4.5 and RCP8.5. Figure \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e shows the classification of JJA days from the model output into the thirteen synoptic weather patterns. There is no significant difference in how the days are classified between scenarios RCP4.5 and RCP8.5 for each model. The HIRHAM model, which best estimated the synoptic weather patterns in comparison with the ERA5 reanalysis, simulates an increase in JJA days in type 12 in both scenarios, going from 42.09% in the historic CORDEX to 47.4% for RCP4.5 and 44.88% for RCP8.5. However, REMO and WRF do not simulate significant increases or decreases in type 12 with respect to ERA5. CORDEX models agree that RCP8.5 increases types 05 and 11 in comparison with RCP4.5, which are patterns with northeast advections and cyclones. On the other hand, the CORDEX models simulate a decrease in the number of days of types 06, 09 and 13 in the case of RCP8.5, which are east advections and pure anticyclones.\u003c/p\u003e\n\u003cp\u003eThe Mann\u0026ndash;Kendall test was run for scenarios 4.5 (Table S8) and 8.5 (Table S9). A detailed description of all the model trends is found in supplementary material S10. Although in the CORDEX historical dataset, there are no significant trends, the CORDEX future datasets show some possible significant increasing and decreasing trends. In RCP4.5, HIRHAM is the only model that shows significant trends, which are on synoptic weather type 02 (SDT), type 12 (SIT) and type 13 (SDT). Type 12, which contains the highest number of days, shows an increasing trend from 43.37\u0026ndash;50.57% of the JJA days. This increasing trend is not shown by WRF or CORDEX. RCP8.5 has more significant trends in all the CORDEX models. The most remarkable ones (due to the high percentage of days) are the SDT for type 02 (shown both in HIRHAM and REMO), the SIT for type 11 (shown in HIRHAM dataset) and the significant trends shown by type 12, which conflicts WRF (SDT) and REMO (SIT).\u003c/p\u003e\n\u003cp\u003eNext, the temperature percentiles of both scenarios 4.5 and 8.5 are analyzed to subsequently select the HW potential days. CORDEX models show an increase in temperatures in all their percentiles (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e), but there are differences among them. For scenario 4.5, WRF simulations result in a small increase in the 95th percentile (+\u0026thinsp;0.69\u0026deg;C) in the last climatic period (2071\u0026ndash;2100), reaching 31.57\u0026deg;C in comparison to the first period (2011\u0026ndash;2040), which reaches 30.59\u0026deg;C. For HIRHAM, the 95th percentile increases by +\u0026thinsp;3.17\u0026deg;C (from 30.53\u0026deg;C to 33.7\u0026deg;C), and REMO increases by +\u0026thinsp;0.99\u0026deg;C (from 30.58\u0026deg;C to 31.57\u0026deg;C).\u003c/p\u003e\n\u003cp\u003eIn scenario 8.5, the CORDEX models simulate a higher increase in temperatures for all percentiles. The 95th percentile for the WRF model rises 3.18\u0026deg;C, ranging from 30.9 to 34.08\u0026deg;C, HIRHAM shows an increase of 5.7\u0026deg;C (from 30.79 to 36.49\u0026deg;C) and REMO also considers an increase of 3.89\u0026deg;C (from 30.59 to 34.48\u0026deg;C). All the CORDEX models and scenarios agree in an increase in HW thresholds, which are higher in scenario 8.5. For scenario 4.5, the increase in HW thresholds by periods of 10 years ranges from +\u0026thinsp;0.12\u0026deg;C/10Y (WRF) to +\u0026thinsp;0.53\u0026deg;C/10Y (HIRHAM), while in scenario 8.5, the 95th percentile ranges from +\u0026thinsp;0.53\u0026deg;C/10Y (WRF) to +\u0026thinsp;0.95\u0026deg;C/10Y (HIRHAM). WRF is the model with the lowest heating, and HIRHAM is the model that increases the most.\u003c/p\u003e\n\u003cp\u003eNext, the PSPA method described in Section \u003cspan class=\"InternalRef\"\u003e3.3\u003c/span\u003e is applied to the three climatic model future simulations for both scenarios. Due to the better representation of the WRF model described in the previous section, the following description only considers this model. A detailed description of all CORDEX models is found in supplementary material S11. The WRF results are represented in Figs. \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e for the RCP4.5 scenario and Figs. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e19\u003c/span\u003e for the RCP8.5 scenario. To reduce the number of plots in the main description, we have chosen only three climatic periods (2011\u0026ndash;2040, 2041\u0026ndash;2070 and 2071\u0026ndash;2100) to represent the whole analyzed period (2011\u0026ndash;2100) despite the seven periods (e.g., 2011\u0026ndash;2040, 2021\u0026ndash;2050, \u0026hellip;, 2071\u0026ndash;2100). The complete sequence for WRF, HIRHAM and REMO can be found in the supplementary material (S12\u0026ndash;S20).\u003c/p\u003e\n\u003cp\u003eIn general, terms, RCP4.5 shows four patterns (SWP_S-S, SWP-D_A, SWP-S_A and SWP-D_AU) in the same manner as the historical dataset. However, there are some differences and tendencies to mention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-S_S: stationary and stable pattern.\u003c/strong\u003e The variance explained by this pattern does not have important changes, remaining the most important pattern (SWP1) with a variance that ranges between 21.71 and 27.42%, while in the historical WRF dataset, it ranges between 20.24 and 22.52%. MSLP shows the same structure as the historical period with a more intense thermal low. Z500 shows the same pattern with a more intense ridge, which is moving to higher latitudes. This fact generates higher geopotential heights in the Iberian Peninsula, with maximum values that increase from 5925 m to 5975 m.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-D_A: dynamic and advective.\u003c/strong\u003e The variance explained by this pattern increases from 8.82\u0026ndash;10.85% in the historical WRF dataset (SWP4) to 12.98\u0026ndash;17.5% in WRF4.5 (SWP2). The MSLP shows a less intense blocking anticyclone that generates a more intense thermal low, covering more terrain in southern Europe. Z500 shows a similar pattern with increased advection due to the more important gradient generated by the north movement of the anticyclonic ridge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-S_A: stationary and advective.\u003c/strong\u003e The explained variance by this pattern remains similar during WRF4.5 (9.9\u0026ndash;11.84%) in comparison with WRF historical (10.87\u0026ndash;11.81%). MSLP shows no differences in the general structure, although the blocking anticyclone is less intense, which generates a stronger thermal low. Z500 shows higher geopotential height values (+\u0026thinsp;50 m in some cases) due to the north displacement of the ridge, although the structure remains the same.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSWP-D_AU: dynamic, advective and undulated.\u003c/strong\u003e The explained variance by SWP-D_AU decreases from 13.61\u0026ndash;14.79% (WRF historical) to 6.59\u0026ndash;9.19% (WRF4.5), which makes this pattern SWP4 (SWP2 in the historical dataset). In this sense, WRF4.5 shows less undulation in the patterns. MSLP shows a similar structure with a slightly increased blocking anticyclone. Z500 shows less advection due to the less undulated pattern and the north shift of the anticyclonic ridge.\u003c/p\u003e\n\u003cp\u003eThe daily maximum temperatures shown in Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e for WRF4.5 result in a significant increase with respect to historical WRF. WRF4.5 simulates the SWP-S_S pattern as the warmest on average in the Iberian Peninsula (26.31\u0026deg;C), followed by SWP-DA_U (25.95\u0026deg;C) and SWP-D_A (25.91\u0026deg;C), which rise 1.93\u0026deg;C, 2.03\u0026deg;C and 1.75\u0026deg;C, respectively, compared to the historic WRF.\u003c/p\u003e\n\u003cp\u003eThe PSPA has also been applied to scenario 8.5. Figures \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e (MSLP), 18 (Z500) and 19 (TMAX) show the SWPs for WRF8.5. A detailed description of WRF, HIRHAM and REMO can be found in supplementary material S21. The complete associated charts can be found in Figures S22\u0026ndash;S30.\u003c/p\u003e\n\u003cp\u003eThe MSLP synoptic structure does not present significant changes in comparison with WRF4.5, although there are remarkable differences at Z500. According to WRF8.5, D_AU explains more variance (10.45\u0026ndash;14.79%) than WRF4.5 (6.59\u0026ndash;9.19%), and at the end of the period, it is the second most important pattern (SWP2). However, the structure of this pattern at Z500 has differences. The S‒SW advection at the Iberian Peninsula that WRF4.5 and WRF historical simulate is not observed at WRF8.5, which simulates an intense anticyclonic ridge located at the west of the Iberian Peninsula. Z500 also shows westerly flows in the other three SWPs, which differ from SW flows from WRF4.5. The maximum geopotential height values are increased, reaching 6000 m in extended parts of the Iberian Peninsula. The MSLP shows more intense and Mediterranean-centered thermal lows, which indicates the intense warming of land and sea.\u003c/p\u003e\n\u003cp\u003eThe temperature values for scenario 8.5 are higher in the mid and late periods in comparison with WRF4.5. The pattern that shows the highest increase is SWP-DA_U, which reaches the highest values in the last period (28.12\u0026deg;C in comparison to 26.26\u0026deg;C in WRF4.5). The SWP-S_S shows a mean temperature value in 2071\u0026ndash;2100 of 27.81\u0026deg;C, which is higher in comparison to the 25.81\u0026deg;C simulated at WRF4.5. The rest of the patterns show less important increases.\u003c/p\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Main synoptic types associated with summer days\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIt is important to determine the most common types of synoptic events occurring during summer days to later classify the synoptic trends of HWs. The results of the classification for the synoptic weather types applied to historical ERA5 data show that type 12 is dominant for summer days, followed by types 11 and 02. The rest of the types represent less than 10% of the summer days. Type 12 is defined as an undetermined pattern with no important advections or influence of a synoptic high- or low-pressure center (Jenkinson \u0026amp; Collison, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). In some cases, these patterns are reinforced by strong irradiation, which generates mesoscalar thermal low pressures in the SW of the peninsula (Hoinka \u0026amp; Castro, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The intense solar radiation due to cloudy weather and the scarce wind on the surface and lower layers is a potential cause of HWs in the AMB (Gil et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the Mann\u0026ndash;Kendall test, this pattern shows a nonsignificant increasing trend, accounting for 37.8% of the summer days in 1951 to 40% in 2020. Types 11 and 02 are the next most frequent patterns on summer days according to ERA5, which are defined as a low or cyclone (thermal low in this case) and anticyclonic western advection, respectively. Thermal lows are generated by intense solar radiation and stability, which rises warm air from lower atmospheric layers, generating a low pressure (Portela \u0026amp; Castro, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), commonly produced after stationarity of type 12. However, western advections are dynamic situations that can advect warm and dry air masses from the interior of the peninsula (Mazon et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), generating potential HW days in the Mediterranean basin. No significant trends are found for types 11 (18.1\u0026ndash;16.3%) or 02 (9.6\u0026ndash;11.1%) in the 1951\u0026ndash;2020 period.\u003c/p\u003e \u003cp\u003eWe found a significant decreasing trend for type 07 (east advection with cutoff low above), a rare synoptic type in all climatic periods (with a frequency of 2.5% in 1951\u0026ndash;1980 to 1.56% in 1991\u0026ndash;2020). North advections (type 04) show a nonsignificant decreasing trend that ranges from 6.2\u0026ndash;4.93%. These results show more stability in late climate periods in comparison with the middle of the XX century, which could potentially translate to more HW potential days due to the reduction in less warm advections. The mid-1970s suffered moderate cooling (-0.03\u0026deg;C/year) that was followed by significant warming (+\u0026thinsp;0.07\u0026deg;C/year) (Lionello \u0026amp; Scarascia, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This trend is reflected in the SWPs we discuss next, in which late periods show warmer and more stable patterns than the first periods.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Main synoptic patterns associated with heatwaves at the regional and urban levels\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe determined that four SWPs explain more than 50% of the HW statistical variability in the climatic periods covering 1951\u0026ndash;2020 at the AMB: two stationary patterns (SWP-S_S and SWP-S_A) that explain 27.9% of the variance and two dynamic patterns (SWP-D_A and SWP-D_AU) that explain 22.6%. The MSLP shows, in all four cases, an anticyclone over the Atlantic that blocks the possible flux of low-pressure centers or weather fronts, generating stability with long periods of cloudless conditions. Due to the latitude of the Iberian Peninsula, cloudless conditions can intensify solar radiation flux and generate positive temperature anomalies, promoting HW conditions (Tomczyk et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). There is also a general presence of thermal lows over the Iberian Peninsula, which are deeper in the case of intense solar radiation. The Z500 variable shows more information about general circulation, such as the presence of ridges and troughs, which could indicate warm or cold intrusions. All four SWPs show an anticyclonic ridge in the Iberian Peninsula, which, depending on its position, generates different directions and intensities of advections in height.\u003c/p\u003e \u003cp\u003eThe most stationary and static pattern associated with HWs is SWP-S_S, with a ridge that covers the entire Iberian Peninsula for at least three days. This pattern reaches a mean temperature of 34.4\u0026deg;C. MSLP shows a blocking anticyclone over the Atlantic and a thermal low over the peninsula. The Z500 from SWPS-S_S shows an intense ridge centered on the peninsula, which due to its stationarity can remain for long periods of time, causing a warm air mass to be generated by the peninsula itself. These synoptic conditions increase the surface sensible heat fluxes warming the air parcels near the surface adiabatically (Zschenderlein et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), resulting in HW events.\u003c/p\u003e \u003cp\u003eThe SWP-S_A is also stationary, although it shows a more advective pattern at Z500 with SW flux. There are two main differences between both stationary patterns (SWP-S_S and SWP-S_A). First, at MSLP, the Atlantic anticyclone is located a few km to the south in case of SWP-S_A. Second, at Z500, there is a greater geopotential height gradient due to the location of the low-pressure center in northern latitudes and the anticyclone ridge in the south. These conditions make a potential HW pattern in the E‒SE of the Iberian Peninsula, while in the NW, there are windy and colder conditions. Some studies have discussed the relation between European HWs and the tropical Atlantic conditions represented in this pattern acting as a forcing, which amplify the residence of blocking regimes and therefore increase the possibility of HWs in southern and central Europe (Cassou et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gil et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e.).\u003c/p\u003e \u003cp\u003eThe SWP-DA_U pattern is caused by a trough NW of the Iberian Peninsula that undulates the general circulation and generates advection from S‒SW. This pattern not only advects warm air from the African continent but also generates intrusions of Saharan dust (Sousa et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which worsens the air quality in the Iberian Peninsula. This pattern generates the warmest HWs registered in this study due to the major south component of advection. The SWP-D_A case is characterized by the lowest surface pressures and thermal lows centered in the Mediterranean region. This is possibly generated by the temperature of the sea, which is warming 20% more than the global average (Lionello \u0026amp; Scarascia, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This pattern also shows SW advection at Z500, which supports the importance of SW advections at 500 hPa in HW episodes due to warm advection.\u003c/p\u003e \u003cp\u003eZooming in at the urban level, the AMB is significantly influenced by the dynamic patterns SWP-D_A and SWP-DA_U (dynamic and advective and dynamic, advective and undulated, respectively). SWP-D_AU for 1981\u0026ndash;2010 reaches a mean value of 35.1\u0026deg;C in the AMB, followed by SWP-D_A for 1981\u0026ndash;2010 (35\u0026deg;C) and SWP-D_A for 1991\u0026ndash;2020 (34.9\u0026deg;C). Cases of S‒SW advections are potentially the warmest patterns in the AMB. The mountain ranges, especially the \u003cem\u003eSistema Ib\u0026eacute;rico\u003c/em\u003e, which is located parallel to the Mediterranean coast, generate a foehn effect throughout the E‒NE of the Iberian Peninsula in the case of W‒SW wind flow\u003c/p\u003e \u003cp\u003e(Pe\u0026ntilde;a et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In that context, prefrontal patterns such as SWP-D_AU intrinsically have an SW flow that maintains warm temperatures and dry air in the AMB but not in the W‒NW of the Iberian Peninsula.\u003c/p\u003e \u003cp\u003eOur analysis shows that there is an increasing trend of HWs in the AMB associated with SWP-S_S, characterized by an intense anticyclonic ridge that covers the Iberian Peninsula and reaches central Europe. This SWP generates a stationary pattern that can last for multiple days, covering a large part of western and central Europe. SWP-D_AU lost some frequency of HWs with respect to the beginning of the period (1951\u0026ndash;1980), contrary to other studies (Zschenderlein et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This discrepancy could be because we are explaining only 50\u0026ndash;55% of the variance in HWs with the four SWPs and do not analyze the remaining 45\u0026ndash;50% of the information, which we consider noise (Wold et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe SWP-D_A pattern shows an increase in thermal lows in the Mediterranean regions affecting the AMB due to intense solar radiation. This fact also indicates a notable warming of the sea temperature compared to the beginning of the analyzed period, which could result in more potential HW days for the AMB. This warming of the sea coincides with (Lionello \u0026amp; Scarascia, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where the Mediterranean area is considered a climate change hot spot due to the expected warming of the region compared to the rest of the planet.\u003c/p\u003e \u003cp\u003eOur analysis of the SWPs associated with HWs in the AMB is limited due to the coarse resolution of reanalysis data, which is available at 0.25\u0026deg; (approx. 25 km) and is thus unable to capture human activity and building materials that exacerbate the effects of HWs (Liu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Morris et al., 2017). In this sense, different downscaling techniques are being investigated at the urban scale, albeit with a high degree of uncertainty (Duch\u0026ecirc;ne et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; le Roy et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), but further work is needed to improve mesoscale and urban scale meteorological simulations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.3. CORDEX historical simulations: Analysis of limitations and advantages\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eBoth model simulations and reanalysis data generally agree in the summer\u0026rsquo;s overall synoptical classification, representing similar SWPs in terms of the locations of high pressures, thermal lows, ridges and troughs. However, we have found some robustness and limitations on the performance of climatic models in simulating the synoptic structure of the HW episodes that are noteworthy. The CORDEX historical simulations show discrepancies among the WRF, REMO and HIRHAM models that are worth mentioning due to the possible overestimation of HW temperatures. Regarding the TMAX variable, a positive BIAS of \u0026gt;\u0026thinsp;1.5\u0026deg;C was found in all models compared to ERA5, even after the Q-Q technique for intermodel comparison was applied. This positive BIAS agrees with what has been found in other studies such as (Lhotka et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in which the 90% quantiles were calculated separately for each simulation to remove the influence of the TMAX bias.\u003c/p\u003e \u003cp\u003eThe MSLP and Z500 variables show similar patterns as ERA5, although the structures are shown in a less defined way (general decrease in geopotential height gradient) as a result of the low resolution of the models. The Z500 variable results in an overestimation of the intensity of the anticyclonic ridges, simulating values ​​that range between 5900\u0026ndash;5925 m, while ERA5 simulates values ​​between 5800\u0026ndash;5825 m. In this way, the three analyzed models tend to shift circulation excessively to the north. On the other hand, when analyzing the MSLP variable, the models tend to underestimate the pressure gradient, simulating more indeterminate patterns. Even so, the models are able to reflect the thermal lows and anti-cyclonic blockings in an approximate way, which contrasts with other studies where the underestimation of these blockings has been demonstrated (Scaife et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have analyzed the potential limitations of HW analysis with CORDEX. The elaboration of composites can lead to multiple limitations that have been discussed in several studies (Lhotka et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These limitations are related to the fact that some effects, such as advections from opposite directions, can be canceled when applying the composite of different HW days. Since HWs can be produced by different synoptic patterns, performing a composite of different days that do not have synoptic similarity between them (despite producing HWs) can be counterproductive and limiting. This is especially important for RCMs in which land‒atmosphere iteration plays a vital role in the development of HWs. In this study, this issue is largely corrected by using the PSPA, which groups the synoptic patterns for statistical proximity, separating these patterns with different synoptic structures and avoiding this \"canceling out\" effect. Some articles (Sf\u0026icirc;că et al., 2017; Zong et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have remarked on the importance of the connections between extremely warm periods and synoptic structures analyzed with PCA and PSPA, such as the Western Mediterranean Oscillation found in (Mohammed et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, we would like to point out the limitations in analyzing HWs at the urban scale. Due to the CORDEX resolution (0.11\u0026deg;), it is not possible to analyze the results at the urban scale. In that sense, some articles (Duch\u0026ecirc;ne et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; le Roy et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) have elaborated downscaling techniques to consider the influence of cities on the local climate (e.g., urban heat island). However, CORDEX simulations show the main HW synoptic patterns in a way that allows an analysis of the main structures of HWs and the possible future trends, as we have shown in this study. In this sense, WRF has been considered the model that best represents the synoptic structure of HWs in terms of similar variance, pressure centers and ridge locations when compared to ERA5.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.4. Trends of future HW events based on CORDEX simulations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhen we analyze the HW episodes of future simulations available from CORDEX based on temperature alone, we find that the 95th percentile increases every period, resulting in warmer HW episodes. For example, HIRHAM4.5 and WRF4.5 predict an increase in the 95th percentile by +\u0026thinsp;0.12\u0026deg;C and +\u0026thinsp;0.53\u0026deg;C every ten years, respectively, for the RCP4.5 scenario. The same trend is more pronounced for RCP8.5, with a potential increment ranging from +\u0026thinsp;0.53\u0026deg;C/10Y for WRF8.5 to +\u0026thinsp;0.95\u0026deg;C/10Y for HIRHAM8.5. This increase in temperature matches the findings of the TICC report (Duran et al., 2017), which estimates an increase of 0.8\u0026deg;C this decade and 1.4\u0026deg;C by 2050 compared to 1971\u0026ndash;2000. In that sense, both the TICCC report and the results obtained in this article point to an increase in extreme temperatures that would result in more intense HWs.\u003c/p\u003e \u003cp\u003eNext, we determine the synoptic weather types associated with summer days of future scenarios. This step is necessary to quantify the tendency of potential HW types, such as type 12, which in ERA5 represents 40% of the summer days and 49.7% of the HW days in the 1991\u0026ndash;2020 period. There are discrepancies between the CORDEX models for types 11 and 12. HIRHAM is the only model that shows a significant increasing trend for type 12, which would result in more potential HW days by the end of this century. However, the most notable trends are the decrease in pure anticyclones (type 13) and east advections, such as types 06 and 07, and the increase in north advections, such as types 04 and 05, indicating a more extreme climate. In that sense, all models suggest a slight tendency toward an increase in atmospheric dynamism in the summer months of JJA, generating a slightly more extreme climate than the current climate. This is especially the case for RCP8.5, where we see more advective types, such as north and northeast advections. This change in climate extremes has been found by other studies: (Wang et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) used Student\u0026rsquo;s test to show that climate change features an increase in warm extremes, and (Donat et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) confirmed that there is high confidence that temperature extremes have been warming since the middle of the twentieth century. In the mentioned works, they found a significant correlation between the changes in climate extremes and global warming. In our study, we find a similar relation, especially in RCP8.5. We see a major number of days classified as type 12 (+\u0026thinsp;1.75%) and type 11 (+\u0026thinsp;1.66%), which are considered potential HW types but, at the same time, an increase in cold advections from the north (+\u0026thinsp;2.48%) in the 2011\u0026ndash;2100 period.\u003c/p\u003e \u003cp\u003eThe PSPA for the CORDEX models gives us some insight into potential trends associated with HWs in the 2011\u0026ndash;2100 period. The Z500 variable shows an increase in geopotential height, especially in scenario 8.5. The maximum geopotential height ranges between +\u0026thinsp;50 and +\u0026thinsp;75 m in RCP4.5 and from +\u0026thinsp;75 to +\u0026thinsp;150 m in RCP8.5. This suggests that the anticyclonic ridge will shift to the north, especially in RCP8.5, advecting warm air from northern Africa. It has been proven in multiple studies (Fischer et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Serrano-Notivoli et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that positive 500 hPa height anomalies are the main contributor to the increase in temperatures during HWs, which agrees with what we have found in this study, where geopotential heights are increased at the end of the century. Some articles have related the increase in extreme patterns, such as anomalous anticyclonic ridges, with the waviness of both polar and subtropical jets (Maher et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Martin, 2022). According to these studies, both jets become wavier, while there are no significant trends in their average speeds. This effect generates large-scale circulation anomalies that are intrinsically linked to the HWs. Both ERA5 and CORDEX simulations show that the variable MSLP indicates an increase in thermal lows not only over the south of the peninsula, as pointed out by (Jerez et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) but also over the Mediterranean region, further exacerbating HW events in the AMB.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis study analyzes the past and future HWs from multiple datasets: ERA5 reanalysis (historical period 1951-2020), CORDEX historical (1951-2000) and CORDEX future (RCP4.5 and RCP8.5 for the 2011\u0026ndash;2100 period). We have evaluated the synoptical structure and significant trends associated with HW episodes to better understand the potential HW patterns and the possible evolution not only in the recent past but also in the future. The key findings can be summarized as follows:\u003c/p\u003e\n\u003cp\u003e-The historical analysis (1950-2020) using ERA5 data concluded that the most dominant synoptic weather type for summer days in the Metropolitan Area of Barcelona is type 12 (undetermined pattern without influence of high or low pressures), followed by types 11 (thermal low or cyclone) and 02 (anticyclonic western advection).\u003c/p\u003e\n\u003cp\u003e-The PSPA analysis based on historical ERA5 data from 1950\u0026ndash;2020 identified some specific patterns associated with the development of HW events. Four patterns were found for each climatic period, representing at least 50% of the total HW variance. The four patterns are divided into two groups: stationary patterns (S_S and S_A) and dynamic patterns (D_A and D_AU).\u003c/p\u003e\n\u003cp\u003e-The HW patterns with the highest temperatures on the AMB are the dynamic cases, with maximum mean temperatures of 35 \u0026deg;C. The dynamic patterns (D_A and D_AU) show advections from SW‒W that increase the temperature, especially in the Mediterranean region. However, we have found that the stationary patterns with deep anticyclonic ridges increase the temperatures in inland areas of the Iberian Peninsula. These stationary patterns can remain for multiple days over the Iberian Peninsula, generating inland warming due to intense solar radiation.\u003c/p\u003e\n\u003cp\u003e-The historical CORDEX simulations, once corrected with the Q-Q technique, provide SWPs similar to those of ERA5, showing good model performance in general. This performance allows us to analyze possible future trends according to the CORDEX models for both the RCP4.5 and 8.5 scenarios. However, we found that overestimation of the geopotential height could result in overestimation of the temperatures.\u003c/p\u003e\n\u003cp\u003e-The evolution of the HWs, according to the PSPA applied to CORDEX RCP4.5 and RCP8.5, has a tendency toward the intensification of the anticyclonic ridge, which reaches geopotential values higher than 6050 m. This increase in the geopotential height could generate an increase in temperatures in the lower layers of the atmosphere.\u003c/p\u003e\n\u003cp\u003e-The PSPA analysis for CORDEX future simulations shows that the S‒SW advections caused by a trough in the NW of the Iberian Peninsula will be less frequent in the case of HWs at the end of the century in comparison with the present. This fact contradicts the synoptic weather type analysis, in which RCP8.5 has an increase in atmospheric dynamism. Specifically, RCP8.5 shows a decrease in anticyclones and an increase in north advections.\u003c/p\u003e\n\u003cp\u003eThis study has evidenced the limitations of applying RCMs at the urban scale for a more robust analysis of synoptic patterns of HWs and hopes to motivate future work to improve methods to determine the development and effect of future HWs in urban areas.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e This work has been made possible thanks to the financial support of the ERC Consolidator, Integrated System Analysis of Urban Vegetation and Agriculture (818002-URBAG). The authors would thank AEMET for the interpolated data from 1800 meteorological stations covering 1951-2020 provided for this work (Spain02 dataset: https://www.aemet.es/es/serviciosclimaticos/cambio_climat/datos_diarios?w=2\u0026amp;w2=0). We acknowledge the World Climate Research Programme\u0026apos;s Working Group on Regional Climate, and the Working Group on Coupled Modelling, former coordinating body of CORDEX and responsible panel for CMIP5. We also thank the climate modelling groups (listed in Table 2 of this paper) for producing and making available their model output. We also acknowledge the Earth System Grid Federation infrastructure an international effort led by the U.S. Department of Energy\u0026apos;s Program for Climate Model Diagnosis and Intercomparison, the European Network for Earth System Modelling and other partners in the Global Organization for Earth System Science Portals (GO-ESSP). Finally, the authors would also thank to Servei Meteorol\u0026ograve;gic de Catalunya for the meteorological datasets provided by their Automatic Weather Stations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work has been made possible thanks to the financial support of the ERC Consolidator, Integrated System Analysis of Urban Vegetation and Agriculture (818002-URBAG).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e Conceptualization: all authors. Data collection: SV. Data analysis: all authors. Visualizations: SV. Writing, review and editing of manuscript: all authors. Supervision: GV and JRM. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analyzed during the current study are available in the URBAG-ICTA PSPA_HW repository, [https://github.com/URBAG-ICTA/PSPA_HW.git].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmengual, A., Homar, V., Romero, R., Alonso, S., \u0026amp; Ramis, C. (2012). A statistical adjustment of regional climate model outputs to local scales: application to Platja de Palma, Spain. Journal of Climate, 25(3), 939-957.\u003c/li\u003e\n\u003cli\u003eAran M, Pe\u0026ntilde;a JC, Tor\u0026agrave; M (2011b) Atmospheric circulation patterns associated with hail events in Lleida (Catalonia). 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Quarterly Journal of the Royal Meteorological Society, 145(724), 2973-2989.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CORDEX, ERA5 reanalysis, multivariate analysis, climatic trends, heatwaves","lastPublishedDoi":"10.21203/rs.3.rs-2539070/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2539070/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeatwaves (HWs) are expected to increase both in duration and intensity in the next decades, but little is known about their synoptic and mesoscalar behavior, which is especially important in mid-latitude regions. Most climate research has focused on temperature analysis to characterize HWs. We propose that a combination of temperature and synoptic patterns is a better way to define and understand HWs because including atmospheric circulation patterns provides information about different HW structures that can irregularly affect the territory, and illustrate this approach at the regional and urban scales using the Iberian peninsula and the Metropolitan Area of Barcelona as case studies. We first select HW events from 1950\u0026ndash;2020 and apply a multivariate analysis to identify synoptic patterns based on mean sea level pressure, geopotential height at 500 hPa, and maximum daily 2 m temperature. The results indicate that four synoptic patterns reproduce at least 50% of the variance in HWs, namely, \u0026ldquo;stationary and stable\u0026rdquo;, \u0026ldquo;dynamic and advective\u0026rdquo;, \u0026ldquo;stationary and advective\u0026rdquo;, and \u0026ldquo;dynamic, advective and undulated\u0026rdquo;. Next, we apply the analysis to the Representative Concentration Pathway future scenarios (RCPs) 4.5 and 8.5 from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine how these synoptic trends can change in the future. The analysis shows that the four synoptic patterns continue to explain 55 to 60% of the variance in HWs. Future HW events will be characterized by an increase in geopotential height at 500 hPa due to the northward shift of the anticyclonic ridge. This is especially true for RCP8.5, which simulates business as usual incrementing fossil fuel use and additionally shows an increase in atmospheric dynamism in north advections from all directions in comparison with RCP4.5. These findings point to the importance of considering the geopotential height in HW prediction, as well as the direction of advections.\u003c/p\u003e","manuscriptTitle":"Analysis of synoptic weather patterns of heatwave events at regional and urban scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-02-13 23:24:32","doi":"10.21203/rs.3.rs-2539070/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-04-13T12:37:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-04-13T12:30:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"452e6306-5c1d-4373-8ccf-805165c7bd4c","date":"2023-03-29T15:26:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-03-10T12:43:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8616b765-34ef-4c44-a948-ce8c411d4f68","date":"2023-03-05T08:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-03-02T05:39:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-03-02T05:31:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-02-10T07:34:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2023-02-01T14:23:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bb6a8ba5-b31a-41f1-8fae-34e72c96ce35","owner":[],"postedDate":"February 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-2539070","link":"https://doi.org/10.1007/s00382-023-06828-1","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2023-05-19 20:52:44","publishedOnDateReadable":"May 19th, 2023"},"versionCreatedAt":"2023-02-13 23:24:32","video":"","vorDoi":"10.1007/s00382-023-06828-1","vorDoiUrl":"https://doi.org/10.1007/s00382-023-06828-1","workflowStages":[]},"version":"v1","identity":"rs-2539070","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2539070","identity":"rs-2539070","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}