IntroductionGlobal average temperature is projected to increase approximately 1.5°C from preindustrial levels between 2030 and 2052 at the current rate (IPCC 2018), with related changes in climate at both global and regional scales (IPCC 2018; USGCRP 2018). In addition to other affected sectors, such as agriculture, human health, and ecosystems (IPCC 2018), civil and environmental engineers need to consider the implications of climate change during design, construction, operation, and management of infrastructure (ASCE-CACC 2015; ASCE Task Committee on Future Weather and Climate Extremes 2021). Because most of the engineering procedures in place today rely on regional climate data that reflect the past (ASCE-CACC 2015; Lopez-Cantu and Samaras 2018), consideration of future climate conditions is needed in engineering analyses to ensure safe and reliable infrastructure (ASCE-CACC 2015; Wright et al. 2019, 2021).An approach increasingly used for engineering analyses is to obtain projections of future climate conditions from physics-based climate simulation models—general circulation models or global climate models (GCMs) (Drum et al. 2017; IPCC 2018; Taylor et al. 2012). A growing number of studies have used GCM climate projections for various infrastructure engineering applications, including analyses on bridges (Wright et al. 2012), buildings (Hosseini et al. 2018), water facilities (Vogel et al. 2016), pavement (Underwood et al. 2017), power plants (Bartos and Chester 2015; Van Vliet et al. 2016), power lines (Bartos et al. 2016), rail networks (Chinowsky et al. 2019), urban planning (Zhang and Ayyub 2018), and others.Progress has been made to incorporate climate model projections in engineering procedures, as well as in local, regional, and national climate change adaptation plans. Some studies have investigated the general approaches of applying climate model projections to engineering designs with specific guidance [e.g., Kilgore et al. (2019) and Martel et al. (2021) on hydrologic designs], although approprirately applying regional climate projections remains challenging (Kirchhoff et al. 2019). Institutional and interdisciplinary networks, such as the Infrastructure and Climate Network (Daniel et al. 2014) and Science for Climate Action Network (Moss et al. 2019), represent some of the ongoing efforts to use recent developments from climate science in practical applications. In addition to the National Climate Change Assessment (USGCRP 2018), GCM projections have been used and presented in local climate change adaptation plans for evaluating and planning of public assets and operations (e.g., City of Austin 2018), while some researchers and organizations have developed tools for accessing these projections [e.g., Climate Toolbox by Broward County (2020)].The use of climate projections for engineering designs has also been incorporated into some engineering codes and design manuals, e.g., the use of rainfall projections for stormwater design has been included in the City of Pittsburgh Stormwater Code and Ordinance (City of Pittsburgh 2021), and the City of Vancouver Engineering Design Manual has specified that climate projections should be incorporated for determining the functional lifespan of projects (City of Vancouver 2019). Cities and states have worked with local institutes and universities to obtain regional future climate projections, e.g., the precipitation assessment tool developed by DeGaetano and Castellano (2017) for New York State, the climate projections used in Boston’s climate adaptation plan with joint efforts from the Boston Research Advisory Group (City of Boston 2016), and regional climate change information for Indianapolis (City of Indianapolis 2019) provided by one National Oceanic and Atmospheric Administration (NOAA) Regional Integrated Sciences and Assessments (RISA) program. The RISA program for the Great Lakes region has developed so-called Consumer Reports-style documents, aiming to provide general criteria and strategies on using climate model projections that match specific requirements (Briley et al. 2020).At the national level, various federal agencies, such as the USGS (Terando et al. 2020), also contribute to the practical applications of climate model projections by providing recommendations (e.g., using the results from multiple GCMs with different future scenarios) and developing plans and guidance for monitoring and revisiting the use of climate model projections. The ongoing Climate-Resilient Buildings and Core Public Infrastructure Initiative (Cannon et al. 2020) undertaken by the National Research Council of Canada, in collaboration with Environment and Climate Change Canada and other agencies, aims to develop and incorporate forward-looking climate projections to update engineering codes and standards at the national level.While existing recommendations and tools are informative and effective, the underlying assumptions for GCMs, different climate model projection results, and the projection processing techniques involved in engineering applications are complex and the decisions made regarding these different results and processing techniques vary among different studies. For example, when assessing precipitation extremes for the Northeast US, different data sets and data-processing techniques can be utilized in different studies (Lopez-Cantu et al. 2020; Tryhorn and Degaetano 2011; Wu et al. 2019). Given the large number of GCM data sets and data-processing techniques, engineers are confronted with challenges of reviewing and making various decisions to consider regional future climate conditions (Hyman et al. 2014; USGAO 2016).An overview of how GCM projections were used in existing engineering studies, examining the different steps of decisions and choices involved, can help provide a general background for engineers. Such an overview can also provide a benchmark for reviewing and comparing past or future estimates of climate change conditions as GCM modeling and the procedures of applying GCM projections in engineering applications are revised and improved.Additionally, assessing the different possible outcomes in applying GCM projections with respect to using different data sets and data-processing techniques is important. The uncertainties of applying climate model projections in engineering applications are not well understood (Douglas et al. 2017), and these uncertainties can be challenging when it comes to updating engineering designs (Underwood et al. 2020). It is worth noting that the methodologies for applying climate model projections and evaluating related uncertainty are a rich topic in the climate science literature (e.g., Hawkins and Sutton 2016; Jack and Katragkou 2019; Maraun 2016). Evaluation of the cascading uncertainties from the multiple stages of decisions involved when applying climate model projections for engineering analyses is limited. While comprehensive sensitivity analyses and uncertainty assessments of engineering applications of climate model projections are desirable, such undertakings would be very time and resource intensive and in many ways constrained by available data. Limited-scope sensitivity analyses and uncertainty assessments can still yield useful insights. For example, analysis of the implications of different decisions about GCM projections and postprocessing techniques for selected engineering applications at particular locations can provide some generalizable information and insights and enhance the understanding of the use of climate model projections.The main objective of this study was to provide an overview of how climate model projections have been and can be used in practice among different civil and environmental engineering sectors, including (1) summarizing general procedures and the different stages of decisions involved, (2) categorizing commonly used projection products and processing techniques, (3) presenting the sensitivity of the results when different data sets and processing techniques are used, and (4) offering preliminary observations and recommendations for future applications based on experience to the present. This work aimed to provide an overview of current practices and knowledge gained to date, general information about methods of applying climate model projections (mainly on temperature and precipitation) in a broad range of engineering practices (instead of focusing on particular applications), and insight into the uncertainties involved and importance of making cautious decisions. This objective was accomplished by performing a review of how temperature and precipitation projections have been used in studies across different civil and environmental engineering sectors and by conducting two case studies in which the options available and decisions needed for such applications are demonstrated and the sensitivity and uncertainty of the results assessed.Practices of Applying Climate Model Projections in Infrastructure EngineeringA summary of typical procedures to incorporate climate model projections in engineering applications is provided in Fig. 1, with three major parts: determining requirements for climate variables, obtaining climate model projections, and postprocessing climate projections. Some application-specific requirements, such as spatial location and time frame, are important criteria to consider when applying future climate projections, need to be decided up front, and are indicated in the top left box of Fig. 1.A literature review yielded 50 engineering applications of climate model projections, which are listed in Table 1. The 50 applications were identified with an emphasis on more recent studies and on covering different civil and environmental engineering sectors affected by changing conditions of temperature and precipitation (ASCE-CACC 2015; ASCE Task Committee on Future Weather and Climate Extremes 2021). Additionally, the kinds of climate model projections and methods of using them (corresponding to Fig. 1) in the 50 applications were identified and are presented in Fig. 2. Note that the decisions presented in Fig. 2 were identified to the extent they could be discerned for these 50 studies; the decisions made on the variable formats were not included in Fig. 2 because the formats of the utilized climate projection results were often not explicitly described in the 50 studies (Appendix S2). More detailed descriptions of Fig. 1 and the different use of climate model projections in the 50 applications are provided in subsequent subsections.Table 1. Review of engineering studies in which climate model projections of future temperature or precipitation were usedTable 1. Review of engineering studies in which climate model projections of future temperature or precipitation were usedSectorIDTopicStudyLocationTime frame(s)Buildings and other structures1Building energy with roof designHosseini et al. (2018)MontrealMainly 2018–20372Building energy useReyna and Chester (2017)Los Angeles County2020–20603Building energy useShen (2017)Four sites in US2040–20694Carbonation of concrete structuresTalukdar and Banthia (2016)Four US cities2015–20755Corrosion of steel structuresNguyen et al. (2013)Two cities in Australia1990–21006Energy and building performanceJentsch et al. (2013)Example UK and worldwide sites2020s, 2050s, and 2080s7LandslidePeres and Cancelliere (2018)Peloritani Mountains, Italy40-year periods up to 21008Urban heat island effectZhang and Ayyub (2018)Washington, DC20-year periods up to 20999Urban planningCarter et al. (2015)Manchester, UK2050sCold regions10Alaska infrastructuresMelvin et al. (2017)Alaska2015–209911Design ice loadsJeong et al. (2019)North America30-year periods up to 208312PermafrostHjort et al. (2018)Arctic2041–206013Rain-on-snow floodMusselman et al. (2018)Western North America2071–2100Energy14Electricity distribution with wood polesMerschman et al. (2020)Three US cities2010–210015Electricity systems with temperatureSathaye et al. (2013)California2070–209916Electricity transmission capacityBartos et al. (2016)US2010–210017Electric power supplyBartos and Chester (2015)Western USMainly 2040–206018Peak electricity demandAuffhammer et al. (2017)US2086–209919Peak electricity demandBurillo et al. (2019)Los Angeles County2021–2040 and 2041–206020Power generation systemsVan Vliet et al. (2016)WorldMainly 2040–206921Water stress for power productionGanguli et al. (2017)USUp to 2035Transportation22Aircraft takeoff performanceCoffel et al. (2017)19 worldwide sites2060–208023Bridges with floodsWright et al. (2012)US2010–2055 and 2055–209024Culvert with wildfire debrisFHWA (2017)Canyon Cove Lane, CO30-year periods up to 210025PavementUnderwood et al. (2017)US30-year periods up to 209926PavementFHWA (2016b, a)Example sites in Texas and Maine2010–209927PavementMallick et al. (2014)One site in New Hampshire1970–1999 to 2040–206928PavementMeagher et al. (2012)4 sites in US2041–207029Rail networksChinowsky et al. (2019)US20-year periods up to 210030Rail networksPalin et al. (2013)Great Britain2030–205931Roadway network with flash floodsKermanshah et al. (2017)Five US cities2006–2100Urban water systems32Detention basinMoglen and Rios Vidal (2014)One site in Washington, DC2041–207033Water distribution systemsBondank et al. (2018)Phoenix and Las Vegas2020–205034Water utilitiesVogel et al. (2015)—NYCDEPNYCN/A35Water utilitiesVogel et al. (2015)–PWBPortland, Oregon2006–210036Water utilitiesVogel et al. (2015)–SPUSeattleN/A37Water utilitiesVogel et al. (2015)–TBWTampa Bay, FloridaN/A38Sewer overflowFischbach et al. (2017)Pittsburgh2038–204739StormwaterCook et al. (2017)Pittsburgh2040–207040StormwaterZahmatkesh et al. (2015)One watershed in NYC2030–205941StormwaterRosenberg et al. (2010)Washington State2020–2050Water resources42Eutrophication with precipitationSinha et al. (2017)US and world2031–2060 and 2071–210043Freshwater algal bloomsChapra et al. (2017)US20-year periods up to 209944Ground water resourcesShrestha et al. (2016)Mekong, Vietnam5-year periods up to 210045Water qualityGelda et al. (2019)Two watersheds in NYCMainly 2041–206046Water qualityBoehlert et al. (2015)US2015–210047Water qualityChang et al. (2015)One reservoir in Taiwan2020–2039 and 2080–209948Water quality and quantityAlamdari et al. (2017)One watershed in Fairfax, Virginia2041–206849Water resource infrastructuresDrum et al. (2017)Ohio River Basin30-year periods up to 209950StreamflowRobinson and Herman (2019)91 sites in Western US2000–2100Determining Requirements for Climate VariablesAs presented in Part a of Fig. 1, the first step is to determine the required climate variables for particular engineering applications, including types of climate variables, temporal and spatial resolutions, and variable formats for the applications. While projections of temperature and precipitation are more commonly used and are also the focus of this study, some applications may require other variables, such as humidity (Meagher et al. 2012) and wind speed (Nguyen et al. 2013). Depending on the applications, required temporal resolutions can also be different, e.g., daily temperature and precipitation series are required as input for process models like rainfall-runoff and water-quality models to assess algal blooms in freshwater (Chapra et al. 2017), and subdaily precipitation series are required to examine changes in the frequency and intensity of subdaily rainfall events (Cook et al. 2017). With respect to the implementation of climate projections, a wide range of climate variable formats is used across applications, including climatic design values (Auld et al. 2010) or time series used for engineering process models (Appendix S2).The included variables, temporal resolutions, and spatial resolutions of the projections used in previous studies are provided in Fig. 2. The required spatial and temporal resolutions of the applications can limit available climate projection products (especially for some earlier studies in Table 1), while some further processing techniques are available and can be used to tailor the temporal and spatial resolution of climate projection as desired (i.e., applying postprocessing techniques, discussed subsequently). As presented in Fig. 2, many of the 50 example studies utilized climate projections in daily resolution, although an hourly format may be required for some applications. Many of the applications used projections with spatial resolution of around 10–100 km (generally corresponding to the resolutions of available dynamically downscaled projections and some early developed statistically downscaled projections), whereas more recently developed statistically downscaled projections with finer resolutions (<10 km) were used in some recent studies.Obtaining Climate Model ProjectionsThe next step is to obtain climate model projections for engineering analyses (Part b of Fig. 1). Brief discussions on the background of GCMs (including the sources, scenarios, and downscaling methods) are provided in the first subsection (additional details can be found in Appendix S3), and discussions of the workflow for obtaining GCM projections as presented in Fig. 1 are provided in the second subsection. Note that the sequence of descriptions given in the first subsection on GCMs is slightly different compared to the workflow of Fig. 1 discussed in the second subsection because the availability of GCMs and scenarios differs among various downscaled projection products. For example, a particular downscaled projection product may include results for only one or two climate change scenarios, or it may only include results for a subset of GCMs. It is also worth noting that, though the use of GCM projections is the more common approach to considering future climate conditions in engineering, other forecasting techniques based on historical observations and statistical methods are available and can be utilized for engineering applications (e.g., Cheng et al. 2014; Hu and Ayyub 2018; Lai and Dzombak 2020).GCM-Based Projection MethodsGCMs are physics-based models that simulate various physical processes (such as energy balance and atmosphere–ocean interactions and circulations) with the integration of future greenhouse gas emissions (Flato et al. 2014). Projections from GCMs can be acquired from the World Climate Research Programme’s Coupled Model Intercomparison Project (CMIP), currently at Phase 6 (CMIP6) (Eyring et al. 2016), while many of the existing downscaled GCM projection products are under Phase 5 (CMIP5) (Taylor et al. 2012). GCM projections provide simulation results for different future scenarios, for example, the representative concentration pathways (RCPs) of CMIP5 to represent the uncertainty in future greenhouse gas concentrations (Moss et al. 2010) and the shared socioeconomic pathways (SSPs) (O’Neill et al. 2017) of CMIP6 for further consideration of the socioeconomic scenarios. Multiple GCMs are available for particular CMIP phases and climate change scenarios, and consequently, engineering applications can apply several GCMs to create an ensemble (Taylor et al. 2012) in order to avoid possible large model errors from single GCMs.Because GCMs are limited in spatial (typically >100 km) (ENES 2019) and temporal resolution (typically with daily as the finest scale of resolution) (Taylor et al. 2012), users of climate model projections need to determine whether or which downscaled projection products should be used. Note that discussion of downscaled projections here refers to GCM projections that are processed to have high spatial resolution; the term downscaling sometimes leads to confusion for practitioners (Briley et al. 2015). Two general GCM downscaling techniques are available: regional climate models (RCMs) (dynamical downscaling) or applying statistical techniques (statistical downscaling). Dynamically downscaled GCM projections are produced using RCMs to resolve regional-scale atmosphere–land–ocean coupled physical processes with GCM projections as boundary conditions (Hall 2014), whereas statistically downscaled GCM projections are produced based on statistical relationships between GCM projections and fine-resolution historical observations. Example repositories of dynamical downscaled projections are the North America–Coordinated Regional Climate Downscaling Experiment (NA–CORDEX) (Giorgi and Gutowski 2015) of CMIP5 and the North American Regional Climate Change Assessment Program (NARCCAP) (Mearns et al. 2009) of CMIP3, which include results from simulations conducted with different combinations of GCMs and RCMs. Examples of statistically downscaled GCM projection products in CMIP5 for the US or North America include bias-corrected constructed analogs (BCCAs) (Brekke et al. 2013), localized constructed analogs (LOCAs) (Pierce et al. 2014), and multivariate adaptive constructed analogs (MACAs) (Abatzoglou and Brown 2012).Procedures for Obtaining GCM Projections in Engineering ApplicationsAs presented in Part b of Fig. 1, the general procedures for obtaining GCM projections involve four steps of decisions: sources of GCM results (CMIP phases), downscaling methods, climate change scenarios, and selection and inclusion of particular GCMs. A summary of the different decisions made among the 50 engineering applications of GCM projections is presented in Fig. 2.According to Fig. 2, GCM projections with the use of CMIP5, statistical downscaling, RCP8.5 (or equivalent), and a GCM ensemble were commonly utilized in the 50 applications. While the current CMIP phase of GCMs is CMIP6 (Eyring et al. 2016), the projections used in the 50 applications summarized in Fig. 2 were obtained from CMIP3 or, more commonly, CMIP5, consistent with the CMIP phases for the available downscaled projection products at the time the studies were conducted. Statistically downscaled projection products have been more commonly used, while the particular downscaled projection products used vary. Certain projection products may be preferable for particular types of applications, for example, Kilgore et al. (2019) recommend LOCA for hydrological designs, whereas Gelda et al. (2019) selected MACA because the full set of required variables for water quality modeling is included. Direct use of GCM projections has been applied in some studies, but because of the relatively coarse spatial resolution provided by GCMs, these studies tended to focus on larger regions such as the US. In some cases, postprocessing techniques (to be discussed subsequently) with regional historical observations were applied to process the raw GCM projections for regional assessments. Because dynamically downscaled projection products can provide subdaily results (Cook et al. 2017) and consider regional features, they have also been used when estimates of precipitation extremes were involved (e.g., Alamdari et al. 2017; Jeong et al. 2019). Notably, the justification for utilizing a particular downscaled projection product often was not often specified in previous studies. Scenarios with higher projected concentrations of greenhouse gases, RCP8.5 or equivalent, were used in nearly all of the 50 applications examined, and an intermediate scenario, such as RCP4.5, was often used as well. Obtaining projections from several GCMs to enable engineering analysis using results from an ensemble also was a common approach in previous studies.Postprocessing Climate ProjectionsThe last step is to determine whether or which postprocessing techniques should be applied (Part c of Fig. 1). The use of postprocessing techniques is separated as an additional step because it requires an additional action (or decision) for engineers to process obtained climate model projections, although these techniques can also be categorized as downscaling, bias correction, or model output statistics techniques (e.g., in Maraun et al. 2010). The reasons for applying postprocessing techniques include increasing the alignment between projections and historical observations (Maraun 2016), performing temporal disaggregation (e.g., Coffel et al. 2017), and facilitating the production of particular files, such as weather files from weather generators (e.g., Shen 2017).Common postprocessing techniques used in the identified engineering applications include change factor, quantile mapping, and GCM-modified weather files, as summarized in Fig. 1. For the change factor technique, historically observed climate variables are added (for temperature) or multiplied (for precipitation) by an estimated factor or ratio between GCM historical and future simulations (Maraun 2016). Quantile mapping, on the other hand, modifies the distributions of climate model projections based on a statistical relationship (or transfer function) (Maraun 2016) between distributions of historical observations and GCM historical simulations. Historical observations with a reference period are needed for change factor and quantile mapping techniques. Because weather generators are commonly used to provide weather files for engineering applications, such as in building energy use estimation (Shen 2017), the parameters of weather generators can be modified with the estimates from GCM projections to consider future climate change (i.e., to produce GCM-modified weather files), and these GCM-modified weather files have often been used in these engineering applications. Other postprocessing techniques are also available and have been used, for example, in Hu and Ayyub (2019). Additional descriptions of postprocessing techniques are provided in Appendix S4.Postprocessing techniques have been applied in many studies, as presented in Fig. 2, and these techniques can be applied for different purposes. For example, quantile mapping has been used to further modify the distributions of downscaled projections to reduce bias (Mannshardt-Shamseldin et al. 2012) or to tailor projections to match required temporal resolutions (e.g., Coffel et al. 2017; Meagher et al. 2012).Uncertainties of Applying Climate Model Projections in Infrastructure EngineeringCase Studies of Pavement Design in Los Angeles and Stormwater Drainage Design in New York CityTwo example applications of using temperature and precipitation projections from GCMs in design-related engineering analyses were performed in a preliminary manner to assess and highlight the sensitivity and uncertainties with respect to the decisions involved. The high-temperature performance grade (PG) of asphalt binder for the design of asphalt concrete pavement in Los Angeles (LA) and the diameter of stormwater drainage pipe (for a design of a 1-h storm with 5-year return period) in New York City (NYC) were assessed. The selection of high-temperature PG and the pipe diameter follow calculation procedures similar to those presented in Underwood et al. (2017) and Cook et al. (2020). See Appendix S5 for more technical details on the calculation procedures.Based on Part a of Fig. 1, the following decisions on the requirements of climate variables were made for the two case studies: temperature and precipitation of the two cities, daily resolution (1-h precipitation amount can be estimated using a relationship with 1-day precipitation amount), city-specific projections (i.e., projections at the level of the two downtown National Weather Service stations, LA Downtown and NYC Central Park stations), and climatic design values (i.e., maximum pavement design temperature and 1-day precipitation amount with 5-year return period) were determined. Based on these requirements, the daily projections of temperature and precipitation were obtained for the grid locations closest to the two weather stations. Because the required spatial resolution is at the station level, historical observations for the two weather stations (Lai and Dzombak 2019) were collected (starting from 1878 for LA and 1869 for NYC) and used for postprocessing climate model projections. These historical observations were considered accurate representations of local historical climate conditions and were also used to assess the performance of the projections.The two case studies of the high-temperature PG and the stormwater drainage pipe diameter are presented as illustrative examples. A number of project-specific assumptions were made, and simplified calculation procedures were followed (including the estimation of 1-h precipitation intensity using a relationship with 1-day precipitation amount). The presented results are thus preliminary and subject to limitations. Considering the different observations and projections used, the presented results can be different from the results in Underwood et al. (2017) and Cook et al. (2020), although similar calculation procedures were applied.Assessing Different CMIP PhasesFollowing Part b of Fig. 1, the first step of the analyses was to quantify the variations with respect to different sources of GCMs (i.e., CMIP phases). While many of the commonly used downscaled projection products are from CMIP5, as previously discussed, the downscaled GCM projections for the current phase (CMIP6) are expected to be used in future engineering applications. The assessment of projection results between CMIP5 and CMIP6 therefore provides a baseline for understanding the differences when using the projections of CMIP6. To evaluate the differences between the two phases, the GCM projections of CMIP5 and CMIP6 at the selected grid locations (without downscaling or postprocessing) were compared with the observations in terms of annual average temperature in LA and total precipitation in NYC. Two climate change scenarios (RCP4.5 and RCP8.5 from CMIP5 and the equivalent SSP2-4.5 and SSP5-8.5 from CMIP6) were assessed to provide some additional comparisons, although the selection of scenarios is a subsequent decision, as presented in Fig. 1.Projection results from the GCMs of CMIP5 and their CMIP6 counterparts are provided in Fig. 3. As expected, the raw (low-resolution) GCM projections exhibit a wide range of model bias when directly compared to the regional temperature and precipitation, suggesting that the raw GCM projections should be further processed or downscaled for engineering applications. A visual comparison between CMIP5 and CMIP6 series does not reveal substantial differences in projected trend, with the exception that the temperature projections in CMIP6 seem to exhibit slightly greater increasing trends than those of CMIP5. Some recent studies, such as Meehl et al. (2020), suggest that the GCMs of CMIP6 exhibit greater sensitivity to increases in CO2 concentrations, i.e., with greater temperature changes, than CMIP5. Although further analyses of the derived variables—the pavement temperature and maximum 1-day precipitation amount—are needed, the results of annual average temperature and precipitation are similar between CMIP5 and CMIP6 for the two cities, as presented in Fig. 3.Assessing the Use of Downscaling and Different Downscaled GCM Projection ProductsFollowing Part b of Fig. 1, the performance of downscaling and the differences among the downscaled projection products were assessed. Aligning with the assessment of annual average temperature and precipitation in Fig. 3, the improvement from using a downscaled projection product (LOCA projections, with a resolution of around 6 km) was assessed in Figs. 4(a and b). The LOCA projections were used in this case because LOCA is a more recently developed downscaling method, recommended by Kilgore et al. (2019), and were used in the US National Climate Assessment (USGCRP 2018). To compare the different downscaled projection products, one GCM (CanESM2) was selected (based on its availability in the selected downscaled projection products) and assessed in Figs. 4(c–h) with the projections of RCP4.5 from the original CanESM2 and several downscaled projection products: the results of three RCMs from NA-CORDEX [CanRCM4 (approximately 25-km resolution), RCA4 (approximately 50 km), and CRCM5-UQAM (approximately 50 km)] and BCCA (approximately 12 km), LOCA (approximately 6 km), and MACA (approximately 4 km). The results of probability density functions (PDFs) of daily temperature or precipitation, the estimated design values (maximum pavement design temperature and 1-day precipitation amount with 5-year return period), and the selected increments for the high-temperature PG and pipe diameter are presented in Figs. 4(c–h).The results in Figs. 4(a and b) suggest that downscaling can substantially improve projections at the regional scale, though some differences between the LOCA projections and historical observations can be observed in precipitation. Notably, for the temperature projections in Figs. 4(a and b), the LOCA downscaling substantially improved the GCM projections in terms of bringing the projections to the regional observation level. On the other hand, the LOCA projections of total precipitation in part (a2) exhibit a moderately large bias, suggesting that postprocessing is likely necessary for the precipitation results.Similarly, in Figs. 4(c–h), the downscaling methods reduced the model bias, especially in temperature, though some noticeable differences among downscaled projection products are apparent. The PDFs of daily temperature and precipitation from downscaled projections in Figs. 4(c and f) are notably more comparable to historical observations than the raw GCM projections, with one exception in the BCCA results for precipitation. The results of pavement temperature suggested a substantial improvement with the downscaled projection products (i.e., aligning better with the historical estimates than was the case for the raw GCM projections) in Fig. 4(d), while some moderate improvement was obtained for the precipitation results in Fig. 4(g). However, the various downscaling products can yield quite different results, as was also observed by Lopez-Cantu et al. (2020). For the pavement temperature in Fig. 4(d), statistically downscaled projections generally preserved the long-term future trend exhibited in the GCM, while the results from the three RCMs suggested slightly different trends. For the precipitation in Fig. 4(g), the statistically downscaled projections exhibited slightly greater temporal changes in the future period than the original GCM projections, while the RCM results seemed to project temporal changes more consistent with the original GCM. The results of high-temperature PG and pipe diameter in Figs. 4(e and h) are similar to the results obtained with climatic design values in Figs. 4(d and g): the use of raw GCM projections leads to large model bias, the downscaled projections can reduce the model bias (more improvement in temperature), and the projection products can lead to different estimation results (especially for pipe diameters). Because RCMs include regional features that may not be represented in raw GCMs or in statistically downscaled GCM projections, the RCM-downscaled projections merit particular consideration, although these RCM projections may be less comparable to local historical observations than statistically downscaled projections, such as presented in Fig. 4(c) for the PDFs of daily temperature; in addition, the RCM projections are limited to the number of GCMs and scenarios available compared to the LOCA or other statistically downscaled projections.The model bias from the downscaled projection products exhibited in Fig. 4 was likely caused by a combination of factors, including the limitation of GCMs in producing greater numbers of low-intensity precipitation events, also known as the drizzling effect (Gutowski et al. 2003; Pierce et al. 2014), the limitation of grid-level observations (used for model calibration and downscaling) in representing station-level observations especially for precipitation extremes (Huang et al. 2017), the bias caused by RCMs (Hall 2014), the large variation of future trends projected by RCMs (Karmalkar 2018), and a reduction of spatial and temporal variations in statistically downscaled GCM projections (Gutiérrez et al. 2013). Studies like that of Shepherd (2014) have shown that GCM projections of atmospheric circulations and related phenomena, such as precipitation, are subject to great uncertainty, which can be enlarged at regional scales, and in this case, the downscaled projections of precipitation generally exhibit larger model bias.Based on the results in Fig. 4 (the mean absolute errors and the alignment with observations) and the recommendations from previous studies (e.g., Kilgore et al. 2019), the LOCA projections seem to be a moderately more appropriate option among statistically downscaled projection products and should be considered for use if resources are limited for considering other products. A careful evaluation of the obtained downscaled projections with historical observations is needed, however. Further analyses among different downscaled products and different locations are also needed for a more complete evaluation.Assessing Different Climate Change Scenarios and Selection and Inclusion of GCMsFollowing the workflow presented in Fig. 1, further analyses were conducted considering and using different scenarios and GCMs for the two case studies (the LOCA projections were used). The results are presented in Fig. 5, with the comparisons of results using RCP4.5 and RCP8.5 scenarios from one GCM (CanESM2, with LOCA downscaling) presented in Figs. 5(a–d); and the use of all available 32 GCMs (with LOCA downscaling) presented in Figs. 5(e–h). Additionally, the results from using the median projections from all 32 GCMs as a GCM ensemble are presented in Figs. 5(e–h).The use of a multimodel ensemble provides a so-called consensus climate change trend (Taylor et al. 2012) and more insight into the range of model uncertainty than the use of a single GCM. As presented in Figs. 5(a and c), it is difficult to differentiate the long-term climate change trend from natural variability exhibited in the projection results of one GCM (CanESM2 in this case), especially for precipitation. In comparison, the use of a GCM ensemble, as presented in Figs. 5(b and d), provides climate change trend with less variability and more consistency between scenarios for the near-term period [up to 2050; different scenarios are expected to be similar in a near-term period (Hawkins and Sutton 2009)]. The use of different GCMs additionally provides an assessment of the possible range of model uncertainty, although such model uncertainty can be large especially for precipitation. The estimates of pavement temperature and 1-day precipitation amount using the GCM ensemble in Figs. 5(b and d) also exhibit some model bias compared to the historical estimates. Postprocessing of the LOCA projections may be needed.Assessing the Use of Postprocessing TechniquesAnalyses were conducted on the postprocessed projections, corresponding to Part c of Fig. 1. A change factor method and a quantile mapping method (nonparametric, matching the empirical cumulative distributions with 20 quantiles) were used. Other postprocessing techniques, such as the integration with weather generators, have been investigated in studies like that of Maraun et al. (2010). The objective of these analyses was to assess the performance of postprocessing, possible variations when different postprocessing techniques are applied, and results when different stages of decisions are combined and cascading uncertainties ensue. The results of two case studies using the LOCA projections (32 GCMs) with the two postprocessing techniques are presented in Fig. 6. A separate analysis was also conducted comparing different downscaled projection products with postprocessing, which is further discussed in Appendix S5.Based on the results of Fig. 6 (and additional analyses presented in Fig. S1 of Appendix S5), the postprocessing techniques can increase the alignment with historical observations, though these techniques are also subject to some limitations. In Fig. 6, both the projection uncertainty from different GCMs and projected future trends were modified following their application with postprocessing, especially for RCP8.5, suggesting the postprocessing can inflate future trends (Maraun 2013) and increase the uncertainty level. The selected high-temperature PG and pipe diameter are generally among the three increments, although the particular years for the increases in increment needed vary among the different results in Fig. 6, suggesting the sensitivity of the results with respect to particular postprocessing techniques (Cook et al. 2020) and historical reference periods (Hawkins and Sutton 2016). Additional analyses on the use of raw GCM projections with postprocessing (included in Appendix S5) also suggest that the raw GCM projections after postprocessing are comparable to the downscaled projection results, indicating that postprocessing may reduce so-called added values (Karmalkar 2018) from downscaling.Considering that both statistical downscaling and postprocessing techniques modify the projections based on statistical methods and historical observations, one statistically downscaled projection product is likely sufficient when postprocessing is applied. 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