Changing climate in Italian cities and Italian building regulations: Analysis focused on future climate change scenarios

Changing climate in Italian cities and Italian building regulations: Analysis focused on future climate change scenarios ☆ Krizia Berti a , David Bienvenido-Huertas b,* , Carlos Rubio-Bellido a , Irene RomeroRecuero b a Department of Building Construction II, University of Seville, Seville, Spain b Department of Building Construction, University of Granada, Granada, Spain ARTICLE INFO Keywords: Climate classification Climate zones Degree days Energy demand Italy Climate change ABSTRACT Nowadays, the comfort conditions need to be assured throughout buildings lifetime. The building stock is not designed to cope with the climate variations expected in the coming decades. In this context, the climate classification used by countries to define the climate differences among the various areas of the country is of great relevance. This study analyses the climate classification of Italy under both current and future climate change scenarios. The aim is to show the obsolescence of the current climate classification regarding climate change by adapting the degree-day methodology to the climate data of the RCP 2.6, RCP 4.5, and RCP 8.5 scenarios in 2050 and 2100. The research shows that the degree-day variations predicted for the coming decades could totally change the configuration of the Italian climate zoning. By maintaining the current climate zoning in future scenarios, most municipalities would move at least one climate zone below, encouraging the thermal inefficiency of Italy's building stock in the coming decades and therefore, increasing the risk of energy poverty in the country. 1. Introduction 1.1. Climate classification as a key to energy efficiency Climate change effects are among the greatest obstacles in our society (ISPRA, 2020). According to the International Panel on Climate Change (IPCC), climate change could irreversibly affect life on earth (Tejal Kanitkar and Jayaraman, 2024). Therefore, all countries have set targets to reduce climate change effects through international conventions and agreements such as the Kyoto Protocol and the Paris Agreement (Ki-moon, 2008). According to the IPCC, surface temperatures are projected to increase under all assessed emissions scenarios (Pachauri, 2014). Moreover, heat waves will be more frequent and last longer, and extreme precipitations will be more intense and recurrent in many regions. Oceans will continue to warm and acidify, and sea level rise will continue. One of the main causes of climate change is the high levels of greenhouse gases generated by the building sector (Belussi et al., 2019; Cabeza and Ch` afer, 2020). Only in Europe, the building sector has been responsible for 49 % of total energy consumption (International Energy Agency, 2023)(Directive, 2002) ☆ This article is part of a Special issue entitled: ‘Urban Wind Environment’ published in Urban Climate. * Corresponding author. E-mail address: [email protected] (D. Bienvenido-Huertas). Contents lists available at ScienceDirect Urban Climate journal homepage: www.elsevier.com/locate/uclim https://doi.org/10.1016/j.uclim.2025.102408 Received 17 November 2024; Received in revised form 27 February 2025; Accepted 31 March 2025 Urban Climate 61 (2025) 102408 Available online 10 April 2025 2212-0955/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). (Recast, 2010). Therefore, international agreements are aimed to reducing emissions by up to 90 % by 2050 (Europese Commissie, 2011). Both energy refurbishment and construction standards do not consider climate change effects, thus greatly impacting building stock performance (Walsh et al., 2017). During its life cycle, and after an eventual energy renovation intervention, a building is destined to become obsolete. In fact, the construction solutions adopted according to current climate conditions will not be effective in a few decades as these conditions will be different. In addition, to not guarantee acceptable indoor thermal comfort conditions for users' life, buildings will generate additional costs due to the increased energy consumption and maintenance of envelopes (Maket, 2024; Wu et al., 2025). This condition can also intensify social phenomena such as energy poverty (Siksnelyte-Butkiene et al., 2021). In this context, climate classification is of great relevance. Climate zones can be defined as an area in which climate variables minimally differ among them. Likewise, the climate classification represents a basis for the regulations of every country that refer to it to choose recommendations and mandatory values within the same climate zone to achieve those energy efficiency targets required to reduce climate change effects (Walsh et al., 2017). The characteristics of a climate classification can vary from one country to another, both in terms of the number of climate zones and the methodology used. These classification systems allow us to understand on a macro scale the behaviour of the climate in the energy performance of buildings. Despite the limitations associated with urban microclimates (Wang et al., 2024) and urban heat islands (Rech et al., 2024), their approach allows defining construction design standards. In recent years, researchers have focused their efforts on expanding the existing knowledge of climate classifications. Research carried out in Australia (A. Hurlimann et al., 2024), Canada (Viseh and Bristow, 2023), India (Saifudeen et al., 2023), Borneo (Saadi et al., 2024), Brazil (Walsh et al., 2023) and Spain (Díaz-L´ opez et al., 2021) can be highlighted. Degree-days are usually considered as the input variable (Jim´ enez Torres et al., 2023), although there are approaches that tend to extend to the energy performance of buildings (Bienvenido-Huertas et al., 2021; Bienvenido-huertas et al., 2021) and to various climatic variables, such as humidity (Zheng et al., 2023) and the rainfall index (Hamed et al., 2022). Most of the studies have been oriented towards establishing the criteria for delimiting climate zones: (i) Remizov and Memon (2024) developed a climate zoning misclassification index based on a multivariate grouping of different variables; (ii) Walsh et al. (2022) using the k-means algorithm considering three different zoning levels in the southern USA; (iii) A similar study was carried out by May Tzuc et al. (2023), based on the use of k-means to classify Mexico; (iv) Yang et al. (2024) applied a classification methodology based on Fuzzy C-means (FCM). Also, in some cases, climatic classifications are based on administrative boundaries (Verichev et al., 2020). Thus, much research is based on the use of data mining and AI to obtain climate zones (Remizov et al., 2023). Despite this, the use of existing criteria in current standards can promote a more appropriate transition in energy policies. One of the main barriers that architects face in enabling change includes limited influence, scope, and decision-making capacity (Warren-Myers et al., 2024). In this sense, Carmen Díaz-L´ opez et al. (2021) studied the future evolution of the climate zones of the Spanish Building Technical Code. They were able to determine the possible classifications that the update of the regulations should have. However, the examples of current climate classification analysed do not consider the climate variations expected in the coming decades, so the resulting energy efficiency regulations do not reflect the future reality. A policy framework should adequately respond to the changes generated by climate change (Browne et al., 2024). This policy inadequacy can have social and economic consequences (Hurlimann et al., 2021b). Currently, policy for many built environments has been inadequate to move to action against climate change at the scale that is needed (Hurlimann et al., 2021a). Climate change consequences on the building stock also include a distortion of building energy demand. Several studies have shown that the heating demand will decrease in the coming decades, and the cooling demand will increase considerably due to rising temperatures, thus changing building stock performance compared to the current situation (Tootkaboni et al., 2021). Verichev et al. (2023) stressed the need to understand the future evolution of the climate in order to establish more efficient designs for building façades. Furthermore, in southern European countries, an increase in building overheating is expected in 2050 (Calama-Gonz´ alez et al., 2024). The impact on climate zones has also been analysed in some works. Several studies focused on Spain: Díaz-L´ opez et al. (2021, 2021) studied the limitations of the current climate zones in Spanish buildings and showed their obsolescence related to the climate change effects expected in the future, and Bienvenido-Huertas et al. (2021) and Bienvenido-huertas et al. (2021) analysed the limitations of the current climate classification in the Andalusia region in Spain and created a new climate classification based on the k-means methodology. Likewise, Verichev et al. (2020, 2020) analysed the climate change effects in three regions of southern Chile considering two future climate change scenarios and showed that current climate zones will be replaced by warmer ones, and Zhai and Helman (2019) identified four reference climate models for three temporary scenarios and seven climate zones. The energy climate change effects were seen on a portion of the housing stock in the United States. These studies have shown that the existing climate zoning is not in line with current climate variations. Although these limitations may be due to the lack of climate data in some locations (Gupta et al., 2025), in most cases it is due to the lack of updating of the data. Climate classifications are based on historical data, which may not represent future climate conditions due to climate change (Gupta et al., 2023). Climate change effects can significantly change current building stock performance, thus impacting the effectiveness of the design strategies used in future scenarios. Climate change impact has been recently studied under various future scenarios for a zero-energy building (ZEB), resulting in that energy demand would increase and that a properly sized renewable energy installation is required to achieve a net zero energy balance (da Guarda et al., 2020). This could be significant in areas characterized both by lower current temperatures due to the reduced effectiveness of heating strategies and by higher current temperatures due to the reduced K. Berti et al. Urban Climate 61 (2025) 102408 2 effectiveness of cooling strategies. 1.2. Italian context In Italy, among the policies on emissions reduction, there are documents such as the Piano Nazionale Integrato Energia e Clima (Pniec) “which defines the tools with which the country must equip itself to achieve the European objectives in terms of sustainable energy transition by 2030” (Ministero dello Sviluppo Economico, 2019) and the Strategia Energetica Nazionale 2017, which sets out a series of actions to be fulfilled by 2030 to achieve results in terms of environmental and decarbonization objectives (MISE and MATTM, 2017). In this context, buildings are often inefficient, so building energy demand should be reduced. For this purpose, energy renovation interventions on building envelopes have recently been made. Likewise, the first law on energy-saving was approved in Italy in 1976 (373/76), so there was no regulation before the introduction of thermal insulation in building envelopes, among other issues. According to ISTAT data from the 2011 census, 25 % of the entire building stock in Italy is made up of buildings built before 1946 and 15 % before 1919 (ISTAT, 2021). In other words, most of the Italian residential stock was built before a law regulated energy-saving measures. However, some estimates from CRESME showed that tax incentives for building renovation and energy rehabilitation have affected more than 21 million interventions between 1998 and 2020 (Camera dei deputati XVIII LEGISLATURA, 2020). To determine the energy demand of buildings according to their location in Italy, the national territory was divided into six climate zones (A, B, C, D, E, and F) according to the average daily temperatures (DPR 412/1993, 1993) (Presidential Decree 412/93). Thus, the standard allows for the classification of the 8097 municipalities spread across its twenty regions (Fig. 1). The climatic zones were established with the degree-days methodology (Riva and Murano, 2013). The degree-days were obtained by adding the differential between the base internal temperature (set by convention at 20 ◦C) and the average external temperature: GD =∑(Tint,base −Text,average)(1) Climate zones ranged from A, which corresponds to places characterized by higher average daily temperatures, to F, which corresponds to places characterized by lower average daily temperatures (Fig. 2). Degree-days were divided as follows: zone A was characterized by degree day values below 600. This was the condition of some municipalities belonging to the southern regions. Zone B Fig. 1. Regions of Italy. K. Berti et al. Urban Climate 61 (2025) 102408 3 was characterized by values between 601 and 900, while values in zone C were between 901 and 1400. These two climate zones included the islands, as well as the southern coasts and part of the coasts of the Liguria and Toscana regions. Zone D was characterized by degree day values between 1401 and 2100 and represented the conditions of most coasts. Zone E values ranged from 2101 and 3000, and those in zone F were above 3001. These last two zones were characterized by the most rigid temperatures and included both the north of the country and the central inland areas. In Italy, the reference regulation on building energy certification is DM 26/06/2015, which defines the modalities of applying the calculation methodology on building energy performance, as well as the prescriptions and minimum requirements of the same according to the legislative decree 19 August 2005, for both new buildings and existing buildings rehabilitation (Governo della Repubblica Italiana, 2015). The Presidential Decree 412/93, in addition to defining the division of the country into climatic zones, assigns to each of them a period and a schedule for switching on heating systems. In the year 2022 there has been a reduction in the periods and hours of switching on heating systems for all climate zones (Table 1). Italian regulations set thermal transmittance limits for both buildings in energy refurbishment and new buildings, based on the climatic zone in which the building is located. In all the cases when energy refurbishment is not involved, a reference building is considered. A reference or target building is defined as a building that is identical in terms of its geometry (shape, volumes, floor area, surfaces of building elements and components), orientation, spatial location, intended use and boundary situation. It also has predetermined thermal characteristics and energy parameters. Italian regulations define transmittance values for the different parts of the building envelope: vertical opaque components, towards the outside, non-air-conditioned rooms or against the ground (Table 2); horizontal or sloping opaque roofing components, towards the outside and non-air-conditioned rooms (Table 3); horizontal opaque floor components, towards the outside, non-air-conditioned rooms or against the ground (Table 4); technical and opaque closures of the cassettes, including frames, towards the outside and non-air-conditioned rooms (Table 5); vertical and horizontal opaque separation walls between neighboring buildings or building units (Table 6). The reference regulation also defines the limit value of the total solar transmission factor for windowed components oriented from east to west via south, in the presence of a mobile shield (Table 7). Several studies focused on the correlation between climate zoning and climate change in Italy. Tootkaboni et al. (2021) studied Fig. 2. Degree days in Italy according to Italian regulations. K. Berti et al. Urban Climate 61 (2025) 102408 4 Table 1 Periods and hours for switching on heating systems according to Italian regulations. Climate zone Period Hours A 8 Dec - 7 Mar 5 B 8 Dec - 23 Mar 7 C 22 Nov - 23 Mar 9 D 8 Nov - 7 Apr 11 E 22 Oct - 7 Apr 13 F no limits no limits Table 2 Thermal transmittance U for vertical opaque components, towards the outside, non-air-conditioned rooms or against the ground. Climate zone Energy refurbishment of existing buildings Reference building U (W/m 2 K) U (W/m 2 K) 2015 (1) 2021 (2) 2015 (1) 2019/2021 (3) A, B 0.45 0.40 0.45 0.43 C 0.40 0.36 0.38 0.34 D 0.36 0.32 0.34 0.29 E 0.30 0.28 0.30 0.26 F 0.28 0.26 0.28 0.24 Table 3 Thermal transmittance U for horizontal or sloping opaque roofing components, towards the outside and non-air-conditioned rooms. Climate zone Energy refurbishment for existing buildings Reference building U (W/m 2 K) U (W/m 2 K) 2015 (1) 2021 (2) 2015 (1) 2019/2021 (3) A, B 0.34 0.32 0.38 0.35 C 0.34 0.32 0.36 0.33 D 0.28 0.26 0.30 0.26 E 0.26 0.24 0.25 0.22 F 0.24 0.22 0.23 0.20 Table 4 Thermal transmittance U for horizontal opaque floor components, towards the outside, non-air-conditioned rooms or against the ground. Climate zone Energy refurbishment for existing buildings Reference building U (W/m 2 K) U (W/m 2 K) 2015 (1) 2021 (2) 2015 (1) 2019/2021 (3) A, B 0.48 0.42 0.46 0.44 C 0.42 0.38 0.40 0.38 D 0.36 0.32 0.32 0.29 E 0.31 0.29 0.30 0.26 F 0.30 0.28 0.28 0.24 Table 5 Thermal transmittance U for technical and opaque closures of the cassettes, including frames, towards the outside and non-air-conditioned rooms. Climate zone Energy refurbishment for existing buildings Reference building U (W/m 2 K) U (W/m 2 K) 2015 (1) 2021 (2) 2015 (1) 2019/2021 (3) A, B 3.20 3.00 3.20 3.00 C 2.40 2.00 2.40 2.20 D 2.10 1.80 2.00 1.80 E 1.90 1.40 1.80 1.40 F 1.70 1.00 1.50 1.10 K. Berti et al. Urban Climate 61 (2025) 102408 5 climate change effects on heating and cooling demand as well as the risk of overheating in existing and refurbished residential buildings in Milan. The study showed that cooling demand greatly increased, and heating demand moderately decreased. Likewise, the effects of retrofitting buildings were reduced in future scenarios. Murano et al. (2017), Baglivo et al. (2023), Marco et al. (2023) and Summa et al. (2020a, 2020b) showed that, although retrofitting the building stock reduced heating demand, cooling demand increased. Climate change effects should therefore be considered in both current and future scenarios. Another study showed that current zero energy buildings (ZEB) could become obsolete in the near future. Summa et al. (2020a, 2020b) analysed a ZEB building located in Rome and showed an 18 % increase in annual energy consumption by 2050. Self-consumption could mitigate the increase in cooling load. Mancini and Basso (2020) showed that, in 419 Italian buildings designed to the current standard, the PV installation can offset the cooling energy consumption. However, in the most severe climate scenarios, the surface area of the PV installation exceeds that of most of the buildings they analysed. This poor energy performance could lead to up to 462 thousand Italian households being in energy poverty (Campagnolo and De Cian, 2022), despite palliative measures such as superbonuses (Massacci et al., 2024). These studies stressed the actual need for updating the Italian regulations on climate classification. Not considering climate change means accepting the imminent obsolescence of both the existing refurbished building stock and new buildings. The scientific literature on climate classification has shown how the zones of a country present significant variations with climate change. In the case of Italy, several authors have expressed the need to study this aspect. In this sense, Congedo et al. (2024) based on the climatic zones of Italy to establish different designs in the total transmission of solar energy from glazed panels. In their study, they considered the impact of climate change. The results showed how the total transmission configurations of solar energy for the current climatic zones present low performances in future years. The authors mentioned the need to update the climatic zones of the country to improve the current designs. Thus, the aim of this study is to assess the adjustment level that the current climate classification of Italy could have in present and future climate conditions because of climate change. The study also aims to accurately characterized the evolution of heating and cooling building energy demand according to climate. 2. Methodology To achieve the objectives of this study, the following work phases have been identified with the respective methodologies. 2.1. Phase 1. Climate data generation The software used in this first phase of the study was Meteonorm. This tool generates weather data in the epw format, i.e., EnergyPlus Weather File, for both current and future scenarios. The climate data for each of the 8097 municipalities in Italy were Table 6 Thermal transmittance U for vertical and horizontal opaque separation walls between neighboring buildings or building units. Climate zone –Reference building –U (W/m 2 K) – – 2015 (1) 2019/2021 (3) All zones – – 0.80 0.80 Table 7 Total solar transmission factor g gl+sh for windowed components oriented from east to west via south, in the presence of a mobile shield. Climate zone Energy refurbishment for existing buildings Reference building g glþsh g glþsh 2015 2021 2015 2019/2021 All zones 0.35 0.35 0.35 0.35 Fig. 3. Workflow of the climate data generation process. K. Berti et al. Urban Climate 61 (2025) 102408 6 generated for both current and future climate change scenarios (Fig. 3) for which the years 2050 and 2100 were chosen. As for the future climate change scenarios, three representative concentration pathways (RCP) scenarios with different levels of climate change severity were chosen: RCP 2.6 (low), RCP 4.5 (medium), and RCP 8.5 (high). These scenarios consider various trends in the evolution of greenhouse gas emissions with a global average temperature increase of 0.3 and 1.7 ◦C in the RCP 2.6 scenario, between 1.1 and 2.6 ◦C in the RCP 4.5 scenario, and between 2.6 and 4.8 ◦C in the RCP 8.5 scenario. The RCP 2.6 scenario is the one closest to the Paris Agreement, and the RCP 8.5 scenario is the most unfavorable. 2.2. Phase 2. Degree-day calculation for current and future climate change scenarios After obtaining climate data, which represented the basis of this study, the degree-days of both the current and future climate change scenarios were calculated using the previously generated epw climate data. As mentioned above, this study considered various future scenarios and different various climate change severity levels. For instance, the years 2050 and 2100 and the RCPs 2.6, 4.5, and 8.5 were chosen to obtain a more exhaustive analysis. The formula included in the current Italian regulations on climate classification, which is introduced in section 3.1 of this study, was used to calculate the degree days of the Italian municipalities. More particularly, the basic formula has been adapted to calculate both HDD and CDD to determine the evolution of heating and cooling demand according to climate. The tool used was Rstudio (based on the R programming language), which calculated a large amount of data more quickly by introducing specific codes. The formula used to calculate the heating degree days (HDD) was as follows: HDD =∑n=365 i=1(Tbase,i−Text,average,i)×bi(2) With bi=1→Tbase,i>Text,average,i bi=0→Tbase,i<Text,average,i In the case of heating demand, the base temperature used for the calculations was 20 ◦C. In case a), the base temperature, i.e., the desired indoor temperature, is higher than the outdoor temperature, so the HVAC system should be activated to recover the differential from the outdoor temperature to the desired indoor temperature. In case b), the base temperature is lower than the outdoor temperature, so the activation of the installation is not required. The formula used to calculate the cooling degree days (CDD) was as follows: CDD =∑n=365 i=1(Text,average,i−Tbase,i)×bi(3) With bi=1→Tbase,i<Text,average,i bi=0→Tbase,i>Text,average,i In the case of cooling demand, the base temperature used for calculations was 25 ◦C. In case c), the base temperature is lower than the outdoor temperature, so the HVAC system should be activated to recover the differential from the outdoor temperature to the desired indoor temperature. In case d), the base temperature is higher than the outdoor temperature, so the activation of the installation system is not required. 2.3. Phase 3. Analysis of the evolution of the degree-days between the current and future climate change scenarios in Italian municipalities The data obtained in Phase 2 were statistically evaluated, both descriptively and by means of a concordance analysis. The latter analysis was performed using the kappa coefficient (K). It is used to assess the agreement or reproducibility of measurement instruments whose result is categorical (2 or more categories), and it represents the proportion of agreements observed beyond chance with respect to the maximum possible agreement (Eq. (4)). A K value of 0 is associated with poor agreement strength, from 0.21 to 0.81 it is acceptable, and above 0.81 almost perfect (Landis and Koch, 1977). The agreement between the answers given by the annual and monthly scales could be checked by assessing K. This analysis was carried out for each month of the year. K=P0−Pe 1−Pe (4) where P0is the proportion of observed agreements (Eq. (5)), and Pe is the proportion of expected agreements (Eq. (6)). P0=NZBase scenario−Comparative scenario +ZBase scenario−Comparative scenario N(5) K. Berti et al. Urban Climate 61 (2025) 102408 7 Pe=NZBase scenario •NZComparative scenario +ZBase scenario •ZComparative scenario N2(6) where NZBase scenario−Comparative scenario are municipalities not in the climate zone analysed in either the base or the comparison scenario; ZBase scenario−Comparative scenario are the municipalities in the climate zone under analysis, both in the base scenario and in the comparison scenario; NZBase scenario are the municipalities not in the climate zone analysed in the base scenario; NZComparative scenario are the municipalities not in the climate zone analysed in the compared scenario; ZBase scenario are the municipalities in the climate zone analysed in the base scenario; ZComparative scenario are the municipalities in the climate zone analysed in the compared scenario; and Nis the total number of dwellings. Fig. 4. Scatter plots of heating degree days: comparison between current and future climate change scenarios. K. Berti et al. Urban Climate 61 (2025) 102408 8 Sensitivity and specificity were also assessed in the concordance analysis. Sensitivity is the probability of classifying a municipality in the same climate zone with the two analysis scenarios (Eq. (7)). Sensitivity varies from 0 to 1 (0 to 100 %). The higher the numerical value, the greater the agreement in the classifications of the same climate zone. Specificity is the probability of classifying a municipality in a different climate zone in the two scenarios considered (Eq. (8)). Specificity varies from 0 to 1 (0 to 100 %). The higher the numerical value, the higher the concordance in municipalities classified in another climate zone than the one under analysis. Sensitivity =(ZBase scenario&ZComparative scenario) (ZBase scenario&ZComparative scenario)+(ZBase scenario&NZComparative scenario)(7) Specificity =(NZBase scenario&NZComparative scenario) (NZBase scenario&NZComparative scenario)+(NZBase scenario&ZComparative scenario)(8) 3. Results and discussion 3.1. Climate demand in current and future scenarios Heating and cooling degree days were calculated for all the municipalities in Italy. For this purpose, both the current state and two different future scenarios (2050 and 2100) were considered, as well as three levels of climate change severity, i.e., RCP 2.6, RCP 4.5, and RCP 8.5, for each scenario. HDD values of heating demand decreased in the future scenarios (Fig. 4). In the current scenario, a range of values between 0 and 6088 ◦C was considered, which varied according to the climate scenario and the climate change severity. In RCP 2.6, the maximum value was reduced to 5635 ◦C in 2050 and 5730 ◦C in 2100, although the difference between current and future scenarios was not significant. In this case, most municipalities were characterized by HDD values between 4000 and 1000 ◦C. As for RCP 4.5, i.e., a medium level of climate change severity, was considered, the maximum value of the analysed range was reduced to 5459 ◦C in 2050 and 5013 ◦C in 2100, resulting in an insignificant variation as in the previous case. On the other hand, as for RCP 8.5, the maximum value of the range reached 5288 ◦C in 2050 and 4057 ◦C in 2100. In this case, most municipalities were characterized by HDD values lower than 3000 ◦C in 2050 and lower than 2000 ◦C in 2100. This decrease was also evident in the box-and-whisker (Fig. 5), where the median values also varied in the different scenarios. The current scenario was characterized by a median value of 2584 ◦C, i.e., half of Fig. 5. Box-and-whisker plots of heating degree days: comparison between current and future climate change scenarios. K. Berti et al. Urban Climate 61 (2025) 102408 9 CRediT authorship contribution statement Krizia Berti: Writing – review & editing, Writing – original draft, Visualization, Software, Resources, Methodology, Investigation, Formal analysis, Conceptualization. David Bienvenido-Huertas: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Conceptualization. Carlos Rubio-Bellido: Supervision, Resources, Project Fig. 12. Results of the sensitivity and specificity analysis using the Italian standard climate zones as the baseline scenario. K. Berti et al. Urban Climate 61 (2025) 102408 16 administration, Funding acquisition. Irene Romero-Recuero: Writing – review & editing, Writing – original draft, Visualization. Declaration of competing interest 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. Acknowledgements This work was supported by Own Research Plan ‘Project program for young researchers’ (PPJIA2022-08) of the University of Granada. Appendix A. Climate zones in Italy obtained in the research Fig. 13. Comparison of the number of municipalities per climate zone between the current scenario defined by the regulations and that calculated by this study. K. Berti et al. Urban Climate 61 (2025) 102408 17 Fig. A1. Map of the climate zones in Italy: current scenario. K. Berti et al. Urban Climate 61 (2025) 102408 18 Fig. A2. Map of the climate zones in Italy: scenario RCP 2.6–2050. K. Berti et al. Urban Climate 61 (2025) 102408 19 Fig. A3. Map of the climate zones in Italy: scenario RCP 2.6–2100. K. Berti et al. Urban Climate 61 (2025) 102408 20 Fig. A4. Map of the climate zones in Italy: scenario RCP 4.5–2050. K. Berti et al. Urban Climate 61 (2025) 102408 21 Fig. A5. Map of the climate zones in Italy: scenario RCP 4.5–2100. K. Berti et al. 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