Climate change is increasing the frequency and severity of many climate-related hazards worldwide. The IPCC Sixth Assessment Report (AR6) reports that global surface temperature in 2011–2020 was 1.09°C higher than in 1850–1900, and that this warming has already led to more frequent and intense hot extremes and heatwaves, along with fewer cold extremes. AR6 also reports that heavy precipitation has intensified in many regions, while drought, flooding, tropical cyclones, and compound events such as heat-drought combinations and coastal flooding are becoming more important climate risks (IPCC, 2021). As these hazards continue to evolve, there is a growing need for indicators that can consistently track climate extremes and support climate risk assessment over time.

The Actuaries Climate Index (ACI; American Academy of Actuaries et al., 2018), launched in November 2016, is a climate monitoring metric designed to track long term changes in extreme weather and sea level conditions across Canada and the continental United States. It was jointly developed by a consortium of North American actuarial bodies: the Society of Actuaries (SOA), the Canadian Institute of Actuaries (CIA), the Casualty Actuarial Society (CAS), and the American Academy of Actuaries (AAA). The collaboration was coordinated through a dedicated Climate Change Committee, with support from climate science partners who provided data and helped develop the methodology. Rather than focusing on mean climate conditions, the ACI specifically quantifies changes in the frequency of extreme events, such as unusually high or low temperatures, heavy precipitation, prolonged drought, high winds, and changes in coastal sea level.

The index serves as an objective, transparent, and educational tool, informing actuaries, policymakers, researchers, and the public about changes in the prevalence of extreme climate events. These changes are increasingly relevant for risk assessment, financial modelling, and adaptation planning. By summarising multiple climate extremes into a single consistent metric, the ACI provides a framework for comparing climate risk across regions.

Several studies have explored the applicability of the ACI methodology in Europe. For example, Zhou et al. (2023) developed an actuarial climate index for the Iberian Peninsula based on the North American ACI framework, Garrido et al. (2026) for France, and Rogo et al. (2025) for Italy. These developments highlight the adaptability of the ACI methodology and provide a basis for constructing a comparable index for the UK.

A related measure, the Actuaries Climate Risk Index (ACRI; American Academy of Actuaries, 2020), was later introduced to assess the economic risks associated with climate-related hazards by linking climate indicators with financial loss data.

Following the development of the ACI, the Australian Actuaries Climate Index (AACI; Actuaries Institute, 2025) was first launched in 2018 to monitor changes in the frequency of extreme weather conditions and sea levels in Australia. Although the AACI was inspired by the ACI, its methodology differs due to the use of different climate variables, thresholds, and reference periods reflecting regional climate characteristics; these differences are discussed in Section 6.

The aim of this paper is to construct the ACI for the UK (UKACI) and explore historical trends in extreme weather-related events. The remainder of the paper is structured as follows. Section 2 describes changes in the UK climate. The ACI framework and the data used in this study are outlined in Sections 3 and 4, respectively. Section 5 analyses the weather-related variables included in the ACI and presents the calculation of the index for the UK. In Section 6, we present a sensitivity analysis of the UKACI and compare it with the methodology of the AACI. Finally, Section 7 concludes with a discussion and final remarks.

The United Kingdom has a mild climate, largely influenced by the nearby Atlantic Ocean and prevailing westerly winds. As a result, temperatures are generally moderate compared with other regions at similar latitudes. The long-term average annual temperature for the UK is approximately 9–10°C, although this varies across regions due to latitude, elevation, and proximity to the coast (Kendon et al., 2025).

The United Kingdom has experienced significant climatic changes over recent decades. Observational records indicate that the UK climate has become warmer and wetter compared with much of the twentieth century (Kendon et al., 2025). Notably, the ten warmest years on record in the UK have all occurred since 2002 (Fung & Lowe, 2023; Kendon et al., 2025), highlighting the strong warming trend observed in recent decades. According to the Met Office, the UK’s average temperature has increased by approximately 0.9°C since the period 1961–1990, with the most rapid warming observed since the 1990s. This warming has contributed to an increased frequency of extreme high temperature events and heatwaves, particularly in southern England. Climate projections suggest that temperatures will continue to increase throughout the twenty-first century, with particularly pronounced warming during summer months (Met Office, 2022).

Changes in precipitation patterns have also been documented. Historical records indicate that five of the ten wettest years in the UK since 1836 have occurred since 1998, suggesting an increase in wet extremes in recent decades (Kendon et al., 2025). More recently, during the period 2010–2019, UK summers mean annual precipitation has increased by approximately 11%, while mean winter precipitation has increased by around 4% in terms of total rainfall amount (millimetres) compared with the 1981–2010 baseline (Kendon et al., 2022). These changes are broadly consistent with climate projections that anticipate wetter winters and an increased frequency of heavy rainfall events.

Rainfall patterns across the UK exhibit substantial spatial variability. Although climate change affects the entire country, the distribution of precipitation is highly heterogeneous, reflecting the influence of atmospheric circulation patterns and topographical features. In particular, rainfall amounts tend to be higher along the western coasts, with especially high levels observed in western Scotland, while eastern regions are generally drier.

Overall, the available evidence indicates that extreme weather events, including heavy rainfall and heatwaves, are becoming more frequent or more intense in the UK. These developments highlight the need for robust indicators capable of systematically monitoring changes in climate-related extremes and their potential implications for economic and financial risk.

These climatic changes increase the severity of climate-related physical risks affecting the UK. Perils such as flooding, storms, heatwaves, and droughts are becoming more damaging under changing climatic conditions. These perils can damage buildings and transport networks, disrupt business activity, threaten food security, and increase pressure on public health systems (Kendon et al., 2025; Met Office, 2026). In other words, climate change is not only changing the climate baseline but also worsening the very events that drive physical losses.

The Actuaries Climate Index (ACI) consists of six components, see Table  1, designed to capture changes in different types of climate extremes, including temperature, precipitation, wind, drought, and sea level. Each component is presented as a monthly time series beginning in 1961 and is derived from data collected from a large network of meteorological and coastal tide stations across the United States and Canada (American Academy of Actuaries et al., 2018).

All measurements are evaluated relative to a 30-year reference period from 1961 to 1990, and the ACI is expressed in terms of anomalies from this baseline.

Warm temperature (T90)

This component represents unusually warm temperature events. An increase in the frequency of very warm days is a key indicator of climate warming and reflects a shift in the upper tail of the temperature distribution. It is calculated using both daily maximum temperature (TX) and daily minimum temperature (TN). Since the daily maximum typically occurs during the afternoon and the daily minimum around dawn, TX and TN are commonly interpreted as proxies for daytime and nighttime conditions, respectively.

Warm temperature events are defined as temperatures exceeding the 90th percentile threshold for each calendar day, estimated from a 30-year reference period. The percentile calculation is smoothed using a five-day moving window centred on each calendar day.

For daytime and nighttime temperatures separately, the number of values above the threshold is counted and converted into the percentage of warm days (TX90p) and warm nights (TN90p) for each month.

From the reference period, 12 monthly reference means, μref(j), and 12 standard deviations, σref(j), are calculated, where j represents the calendar month (j= January, February, ..., December).

Standardised anomalies are then computed as

TX90pstd(j,k)=TX90p(j,k)−μrefTX90p(j)σrefTX90p(j)

and

TN90pstd(j,k)=TN90p(j,k)−μrefTN90p(j)σrefTN90p(j)

where j denotes the calendar month and k denotes the year (k= 1961, 1962, ..., 2024).

Because warm days and warm nights tend to occur simultaneously, the final warm temperature anomaly (T90) is defined as the average of the two standardised indices:

T90std(j,k)=12(TX90pstd(j,k)+TN90pstd(j,k)).

Cool temperature (T10)

In contrast to warm temperature events, the cool temperature component represents unusually cold temperature conditions. A decreasing frequency of very cold days indicates a shift in the lower tail of the temperature distribution.

Cool temperature events are defined as temperatures falling below the 10th percentile threshold for each calendar day. The percentile calculation is smoothed using a five-day moving window centred on each calendar day and estimated from a 30-year reference.

The methodology is similar to the warm temperatures calculation, except that the cool temperature threshold is defined using the 10th percentile. Thus, the cool days (TX10p) and cool nights (TN10p) standardised anomalies are calculated as

TX10pstd(j,k)=TX10p(j,k)−μrefTX10p(j)σrefTX10p(j)

and

TN10pstd(j,k)=TN10p(j,k)−μrefTN10p(j)σrefTN10p(j)

where j denotes the calendar month and k denotes the year (k= 1961, 1962, ..., 2024).

The final cool temperature anomaly (T10) is defined as

T10std(j,k)=12(TX10pstd(j,k)+TN10pstd(j,k)).

Precipitation (P)

The precipitation component represents the extreme rainfall and is based on the maximum five-day precipitation (Rx5day) index. The Rx5day is defined as the highest accumulated precipitation over any consecutive five-day period within a given month.

Monthly Rx5day anomalies are standardised relative to the reference period using the corresponding monthly mean, μref(j), and standard deviation, σref(j):

Rx5daystd(j,k)=Rx5day(j,k)−μrefRx5day(j)σrefRx5day(j)

where j denotes the calendar month (j= January, February, ..., December) and k denotes the year (k= 1961, 1962, ..., 2024).

Drought (D)

The drought component represents dry conditions and is quantified using the Consecutive Dry Days (CDD) index. A dry day is defined as a day with daily precipitation less than 1 mm. For each year, the longest dry spell is identified as the annual Maximum Consecutive Dry Days (MaxCDD).

The annual MaxCDD mean, μref, and standard deviation, σref, are calculated from the reference period and used as baseline statistics.

Monthly MaxCDD are obtained by linear interpolation

MaxCDD(12,k)=MaxCDD(k)

and

MaxCDD(j,k)=12−j12(MaxCDD(12,k−1))+j12(MaxCDD(12,k))

where j denotes the calendar month (j= January, February, ..., December) and k denotes the year (k= 1961, 1962, ..., 2024).

Standardised drought anomalies are then calculated as

MaxCDDstd(j,k)=MaxCDD(j,k)−μrefMaxCDDσrefMaxCDD.

Wind speed (W)

Extreme wind conditions are represented using wind power (WP), calculated from daily mean wind speed as

WP=12ρw3

where w is the daily mean wind speed and ρ is the air density (1.23 kg/m 3).

Extreme wind events are defined as wind power values exceeding the 90th percentile threshold. The threshold is estimated for each calendar day using a 30-year reference period as

WPref(i,j)=μWP(i,j,k)+1.28σWP(i,j,k)

where i denotes the calendar day (i= 1, 2,..., 30, 31), j denotes the calendar month (j= January, February, ..., December), and k denotes the year within the reference period.

For each month, the number of days with wind power exceeding the threshold is counted and converted into the percentage of extreme wind (WP90).

Monthly WP90 anomalies are standardised relative to the reference period using the corresponding monthly mean, μref(j), and standard deviation, σref(j):

WP90std(j,k)=WP90(j,k)−μrefWP90(j)σrefWP90(j)

where j denotes the calendar month (j= January, February, ..., December) and k denotes the year (k= 1961, 1962, ..., 2024).

Sea level (S)

The sea level component represents changes in sea level derived from hourly tide-gauge observations at coastal stations. Monthly mean sea level (S) values are calculated from the hourly records.

Monthly sea level anomalies are then standardised relative to the reference period using the corresponding monthly mean, μref(j), and standard deviation, σref(j):

Sstd(j,k)=S(j,k)−μrefS(j)σrefS(j)

where j denotes the calendar month (j= January, February, ..., December) and k denotes the year (k= 1961, 1962, ..., 2024).

This paper is based on station-based observational data spanning 1961–2024 and represents six climate components of the ACI.

Temperature and precipitation data are obtained from the Global Historical Climatology Network Daily (GHCNd; NOAA National Centers for Environmental Information, 2025), providing daily observations from land-based stations. Wind speed data are sourced from the Met Office Integrated Data Archive (Met Office, 2012), where hourly observations are aggregated to daily means and used to calculate wind power. Lastly, the sea level data are obtained from the Permanent Service for Mean Sea Level (PSMSL, 2026; Holgate et al., 2013), providing monthly mean values from coastal tide-gauge stations.

To ensure data quality, only stations with less than 50% missing observations over the study period are retained. Remaining missing values are not infilled and are excluded from calculations. Monthly averages are computed using available stations only; consequently, the number of contributing stations varies over time.

After applying these criteria, the final dataset consists of 98 temperature stations, 106 precipitation stations, 125 wind stations, and 20 tide-gauge stations distributed across the United Kingdom.

The UK Actuarial Climate Index (UKACI) is constructed to represent the overall state of climate extremes in the United Kingdom. Monthly observations are first aggregated across all available stations to obtain national-level time series for each climate component. The UKACI is then computed from these aggregated series as the arithmetic mean of the six components, with equal weighting, following the formulation of the North American Actuaries Climate Index (ACI)1. The cool temperature component (T10) enters with a negative sign to reflect its inverse relationship with warm temperature extremes.

The UKACI is defined as follows:

UKACI(j,k)=16(T90std(j,k)−T10std(j,k)+Rx5daystd(j,k)+MaxCDDstd(j,k)+WP90std(j,k)+Sstd(j,k)).

This section first presents the individual components of the UKACI. It then constructs the aggregated UKACI series over the study period and examines its overall evolution and main trends.

This section assesses the robustness of the UKACI to alternative index constructions, including principal component analysis (PCA), the Australian Actuaries Climate Index (AACI) formulation, and a combined AACI–PCA approach.

This paper develops the United Kingdom Actuarial Climate Index (UKACI) using station-based observations from 1961 to 2024 within the methodological framework of the North American Actuaries Climate Index (ACI). Sensitivity analyses based on principal component analysis (PCA), the Australian Actuaries Climate Index (AACI), and a combined AACI–PCA approach are also conducted to assess the robustness of the index to alternative constructions.

The results show a clear upward trend in the UKACI over the study period, largely driven by warm temperature and sea-level-related components, while precipitation, drought, and wind exhibit greater variability and weaker long-term trends. Although alternative index formulations produce differences in magnitude and component contributions, the overall upward trend remains consistent across methodologies.

The sensitivity analysis highlights that the representation of climate extremes depends importantly on the choice of variables, thresholds, and weighting schemes. In particular, the AACI formulation places greater emphasis on severe tail events through the use of 99th percentile thresholds and modified definitions of precipitation and wind extremes, leading to larger index magnitudes and a stronger role for temperature and precipitation. Similarly, PCA-based approaches assign greater weight to components with higher variability, particularly sea level and warm temperature.

These findings suggest that the ACI framework captures a robust underlying signal of increasing climate extremes in the United Kingdom. However, the contribution of individual components and the interpretation of the index depend on how climate extremes are defined and aggregated. Different formulations therefore emphasise different dimensions of climate risk, with some approaches capturing broader changes across the distribution of extremes and others focusing more strongly on rare, high-impact events.