extreme_rain.R

library(tidyverse)
# ERA5
e5 = read_csv('reanalysis-era5-single-levels-timeseries-sfc9vc2xo5f.csv') |>
  mutate(
    t2m = t2m - 273.15
  ) |>
  group_by(
    valid_time = floor_date(valid_time, unit = "days")
  ) |>
  summarise(
    # t2m = mean(t2m),
    tp = sum(tp) * 1000
  ) |>
  ungroup() |>
  filter(dplyr::between(year(valid_time), 1950, 2014)) |>
  mutate(
    tp_a5 = tp > 5,
    tp_a20 = tp > 20,
    tp_a50 = tp > 50
  )

ey = e5 |>
  group_by(year = year(valid_time)) |>
  summarise(
    n5 = {
      r <- rle(tp_a5)
      sum(r$values)  # Counts all TRUE sequences
    },
    n20 = {
      r <- rle(tp_a20)
      sum(r$values)  # Counts all TRUE sequences
    },
    n50 = {
      r <- rle(tp_a50)
      sum(r$values)  # Counts all TRUE sequences
    },
    annual = sum(tp)
  ) |>
  ungroup() |>
  mutate(
    year_class = cut(year, breaks = 3)
  )

g = ggplot(ey, aes(x = n20, y = annual, color = year_class         )) +
  geom_point(position = position_jitter(width = 0.2), size = 5) +
  scale_color_brewer(palette = 'Reds') +
  facet_wrap(~year_class, ncol = 1) +
  geom_smooth(method = 'lm') +
  theme_bw()