The UK’s Climate Is Warming — and Extremes Are Increasing

Summers are getting hotter and hard-freeze years are increasingly rare, while extreme-heat years cluster after ~1990 signaling rising heat risk.

TidyTuesday

2025

Analyzing 125 years of UK Met Office data reveals rising temperatures, declining frost days, and a surge in extreme heat events after 1990—clear evidence of accelerating climate change.

Published

October 19, 2025

Figure 1: Combined visualization showing UK climate change from 1900-present. The top panel displays four trend charts: maximum temperature rising from ~12.5°C to 14°C, monthly rainfall increasing from 70mm to 80mm, monthly sunshine relatively stable around 115-120 hours, and air frost days declining from ~4 to 2 days. Bottom panel shows a timeline of extreme weather events with dots representing years in the top panel/bottom 5% for each metric. Extreme heat events (orange dots) cluster heavily after 1990, with 6 of 7 occurring after 2000, while extreme cold events (navy dots) become increasingly rare in recent decades. Extreme rainfall events (blue dots) are scattered throughout the period. Annotations highlight post-1990 clustering and the notable 2003 heatwave.

Steps to Create this Graphic

1. Load Packages & Setup

Show code

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
  tidyverse,     # Easily Install and Load the 'Tidyverse'
  ggtext,        # Improved Text Rendering Support for 'ggplot2'
  showtext,      # Using Fonts More Easily in R Graphs
  janitor,       # Simple Tools for Examining and Cleaning Dirty Data
  scales,        # Scale Functions for Visualization
  glue,          # Interpreted String Literals
  patchwork      # The Composer of Plots 
  )})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 12,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

tt <- tidytuesdayR::tt_load(2025, week = 42)

historic_station_met <- tt$historic_station_met |> clean_names()
station_meta <- tt$station_meta |> clean_names()

tidytuesdayR::readme(tt)
rm(tt)
```

3. Examine the Data

Show code

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(historic_station_met)
glimpse(station_meta)
```

4. Tidy Data

Show code

```{r}
#| label: tidy-fixed
#| warning: false

# data prep
met_data <- historic_station_met |>
  left_join(station_meta, by = "station") |>
  mutate(
    date = make_date(year, month, 1),
    decade = floor(year / 10) * 10,
    season = case_when(
      month %in% c(12, 1, 2) ~ "Winter",
      month %in% c(3, 4, 5) ~ "Spring",
      month %in% c(6, 7, 8) ~ "Summer",
      TRUE ~ "Autumn"
    ),
    era = case_when(
      year < 1950 ~ "1900–1949",
      year < 2000 ~ "1950–1999",
      TRUE ~ "2000–present"
    )
  )

# P1: Long-term trends across stations
trends <- met_data |>
  filter(year >= 1900) |>
  summarise(
    avg_tmax = mean(tmax, na.rm = TRUE),
    avg_rain = mean(rain, na.rm = TRUE),
    avg_sun = mean(sun, na.rm = TRUE),
    avg_frost = mean(af, na.rm = TRUE),
    .by = year
  ) |>
  pivot_longer(
    starts_with("avg_"),
    names_to  = "variable",
    values_to = "value"
  ) |>
  mutate(
    variable = recode(variable,
      avg_tmax  = "Maximum Temperature",
      avg_rain  = "Monthly Rainfall",
      avg_sun   = "Monthly Sunshine",
      avg_frost = "Air Frost Days"
    ),
    variable = factor(variable,
      levels = c("Maximum Temperature", "Monthly Rainfall", "Monthly Sunshine", "Air Frost Days")
    ),
    variable_label = case_match(
      variable,
      "Maximum Temperature" ~ "Maximum Temperature (°C)",
      "Monthly Rainfall" ~ "Monthly Rainfall (mm)",
      "Monthly Sunshine" ~ "Monthly Sunshine (hours)",
      "Air Frost Days" ~ "Air Frost Days (days)"
    )
  )

last_points <- trends |>
  group_by(variable_label) |>
  slice_max(year, n = 1, with_ties = FALSE)

# P2: Extreme weather events timeline
annual_summary <- met_data |>
  summarise(
    total_rain = sum(rain, na.rm = TRUE),
    max_temp = max(tmax, na.rm = TRUE),
    min_temp = min(tmin, na.rm = TRUE),
    n_stations = dplyr::n(),
    .by = year
  ) |>
  filter(year >= 1900, n_stations >= 12)

# thresholds
thr <- list(
  rain_hi = quantile(annual_summary$total_rain, 0.95, na.rm = TRUE),
  heat_hi = quantile(annual_summary$max_temp, 0.95, na.rm = TRUE),
  cold_lo = quantile(annual_summary$min_temp, 0.05, na.rm = TRUE)
)

extreme_events <- annual_summary |>
  mutate(
    rain_extreme = total_rain > thr$rain_hi,
    heat_extreme = max_temp > thr$heat_hi,
    cold_extreme = min_temp < thr$cold_lo
  ) |>
  pivot_longer(
    ends_with("_extreme"),
    names_to = "event_type",
    values_to = "is_extreme"
  ) |>
  filter(is_extreme) |>
  mutate(
    event_label = factor(
      case_match(
        event_type,
        "rain_extreme" ~ "Extreme Rainfall",
        "heat_extreme" ~ "Extreme Heat",
        "cold_extreme" ~ "Extreme Cold"
      ),
      levels = c("Extreme Heat", "Extreme Rainfall", "Extreme Cold")
    )
  ) |>
  select(year, event_label)
```

5. Visualization Parameters

Show code

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get basic theme colors
colors <- get_theme_colors(
  palette =  list(
      heat        = "#D55E00",  
      cold        = "#1D3557",  
      temp_warm   = "#E69F00",  
      rain        = "#0072B2",  
      sun         = "#F0E442",  
      frost       = "#56B4E9", 
      neutral_dark  = "#2B2D42",
      neutral_mid   = "#8D99AE",
      neutral_light = "#EDF2F4"
  )
)

### |- titles and caption ----
title_text <- str_glue(
    "The UK's Climate Is Warming — and Extremes Are Increasing"
    )

subtitle_text <- str_glue(
    "Summers are getting hotter and hard-freeze years are increasingly rare, while extreme-heat years cluster after ~1990 \n",
    "signaling rising heat risk.\n"
)

caption_text <- create_social_caption(
  tt_year = 2025,
  tt_week = 42,
  source_text = str_glue(
      "Historical monthly data for meteorological stations, via data.gov.uk",
      "<br>Metric = station means by year | Trends = LOESS (span 0.25) | Extremes = annual 95th percentile"
    )
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.4), color = colors$title, margin = margin(b = 10)),
    plot.subtitle = element_text(face = "italic", family = fonts$subtitle, lineheight = 1.2, color = colors$subtitle, size = rel(0.9), margin = margin(b = 20)),

    ## Grid
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_line(color = "gray90", linewidth = 0.3),
    
    # Axes
    axis.title = element_text(size = rel(0.9), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.95)),
    axis.ticks = element_blank(),
    
    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
        face = "bold", 
        color = "gray20", 
        size = rel(1),
        margin = margin(t = 8, b = 8)
    ),
    panel.spacing = unit(2, "lines"),
    
    # Legend elements
    legend.position = "plot",
    legend.title = element_text(family = fonts$tsubtitle, 
                                color = colors$text, size = rel(0.8), face = "bold"),
    legend.text = element_text(family = fonts$tsubtitle, 
                               color = colors$text, size = rel(0.7)),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code

```{r}
#| label: plot
#| warning: false

### |- P1: trends plot ----
p1 <- ggplot(trends, aes(year, value)) +
  geom_line(color = colors$palette$neutral_mid, linewidth = 0.4, alpha = 0.6) +
  # Geoms
  geom_smooth(aes(color = variable), method = "loess", linewidth = 1.8, se = TRUE, alpha = 0.15, span = 0.25) +
  geom_point(
    data = last_points, aes(year, value),
    inherit.aes = FALSE, size = 2.2, color = "grey20"
  ) +
  # Scales
  scale_color_manual(
    values = c(
      "Maximum Temperature" = colors$palette$temp_warm,
      "Monthly Rainfall"    = colors$palette$rain,
      "Monthly Sunshine"    = colors$palette$sun,
      "Air Frost Days"      = colors$palette$frost
    ),
    guide = "none"
  ) +
  scale_x_continuous(
    breaks = seq(1900, 2020, 40),
    limits = c(1900, 2024)
  ) +
  # Labs
  labs(
    title = "Long-term Trends",
    subtitle = "Smoothed station means with underlying annual variability (1900–present)",
    x = NULL, y = NULL
  ) +
  # Facets
  facet_wrap(~variable_label, scales = "free_y", ncol = 2) +
  # Theme
  theme(
    strip.text = element_text(face = "bold", size = 12, color = colors$palette$neutral_dark, margin = margin(b = 8, t = 4)),
    strip.background = element_rect(fill = colors$palette$neutral_light, color = NA),
    panel.grid.major = element_line(color = colors$palette$neutral_light, linewidth = 0.4),
    panel.grid.minor = element_blank(),
    panel.spacing = unit(1.2, "lines")
  )

### |- P2: timeline plot ----
p2 <- ggplot(extreme_events, aes(x = year, y = event_label, color = event_label)) +
  # Annotate
  annotate("rect",
    xmin = 1990, xmax = 2022, ymin = -Inf, ymax = Inf,
    fill = alpha(colors$palette$neutral_light, 0.35), color = NA
  ) +
  # Geom
  geom_vline(
    xintercept = c(1990, 2003), linetype = c("dashed", "dotted"),
    linewidth = c(0.5, 0.6), color = alpha(colors$palette$neutral_mid, 0.8)
  ) +
  geom_point(
    size = 4.2, alpha = 0.95, stroke = 0.4,
    position = position_jitter(width = 0.2, height = 0.04, seed = 123)
  ) +
  # Annotate
  annotate("text",
    x = 2006, y = "Extreme Heat", label = "Post-1990 clustering",
    size = 3.2, color = colors$palette$neutral_mid, vjust = -3
  ) +
  annotate("text",
    x = 2003, y = "Extreme Heat", label = "2003 heatwave",
    size = 3.0, color = colors$palette$neutral_mid, vjust = 3.6
  ) +
  # Scales
  scale_color_manual(
    values = c(
      "Extreme Heat" = colors$palette$heat,
      "Extreme Rainfall" = alpha(colors$palette$rain, 0.5),
      "Extreme Cold" = alpha(colors$palette$cold, 0.5)
    ),
    guide = "none"
  ) +
  scale_x_continuous(
    breaks = seq(1900, 2020, 20),
    expand = expansion(mult = c(0.02, 0.02)),
    limits = c(1900, 2024)
  ) +
  # Labs
  labs(
    title = "Extreme Weather Events",
    subtitle = "Extreme-heat events surge after ~1990 — clear evidence of a warming climate",
    x = NULL, y = NULL
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_line(color = alpha(colors$palette$neutral_light, 0.9), linewidth = 0.7),
    panel.grid.major.x = element_line(color = alpha(colors$palette$neutral_light, 0.5), linewidth = 0.25),
    panel.grid.minor   = element_blank()
  )

### |- Combined plot ----
combined_plots <- p1 / p2 +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.55),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_text(
        size = rel(0.85),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 0, b = 0)
      ),
      plot.caption = element_markdown(
        size = rel(0.55),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 10)
      )
    )
  )
```

7. Save

Show code

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "tidytuesday", 
  year = 2025, 
  week = 42, 
  width  = 10,
  height = 12,
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] janitor_2.2.0   showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9 
 [9] ggtext_0.1.2    lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1  
[13] dplyr_1.1.4     purrr_1.0.2     readr_2.1.5     tidyr_1.3.1    
[17] tibble_3.2.1    ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       fastmap_1.2.0      gh_1.4.1          
 [5] digest_0.6.37      timechange_0.3.0   lifecycle_1.0.4    rsvg_2.6.1        
 [9] magrittr_2.0.3     compiler_4.4.0     rlang_1.1.6        tools_4.4.0       
[13] utf8_1.2.4         yaml_2.3.10        knitr_1.49         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.5.0          curl_6.0.0         xml2_1.3.6        
[21] camcorder_0.1.0    tidytuesdayR_1.1.2 withr_3.0.2        grid_4.4.0        
[25] fansi_1.0.6        colorspace_2.1-1   gitcreds_0.1.2     cli_3.6.4         
[29] rmarkdown_2.29     crayon_1.5.3       generics_0.1.3     rstudioapi_0.17.1 
[33] tzdb_0.5.0         commonmark_1.9.2   splines_4.4.0      parallel_4.4.0    
[37] ggplotify_0.1.2    vctrs_0.6.5        yulab.utils_0.1.8  Matrix_1.7-0      
[41] jsonlite_1.8.9     gridGraphics_0.5-1 hms_1.1.3          bit64_4.5.2       
[45] systemfonts_1.1.0  magick_2.8.5       gifski_1.32.0-1    codetools_0.2-20  
[49] stringi_1.8.4      gtable_0.3.6       munsell_0.5.1      pillar_1.9.0      
[53] rappdirs_0.3.3     htmltools_0.5.8.1  R6_2.5.1           httr2_1.0.6       
[57] rprojroot_2.0.4    vroom_1.6.5        evaluate_1.0.1     lattice_0.22-6    
[61] markdown_1.13      gridtext_0.1.5     snakecase_0.11.1   renv_1.0.3        
[65] Rcpp_1.0.13-1      svglite_2.1.3      nlme_3.1-164       mgcv_1.9-1        
[69] xfun_0.49          fs_1.6.5           pkgconfig_2.0.3

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2025_42.qmd.

For the full repository, click here.

10. References

Expand for References

Data Sources:

TidyTuesday 2025 Week 42: [Historic UK Meteorological & Climate Data](https://github.com/rfordatascience/tidytuesday/blob/main/data/2025/2025-10-21

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

fonts.R: setup_fonts(), get_font_families() - Font management with showtext
social_icons.R: create_social_caption() - Generates formatted social media captions
image_utils.R: save_plot() - Consistent plot saving with naming conventions
base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils