diff --git a/data-raw/chirps_monthly_afg_adm1_historical.R b/data-raw/chirps_monthly_afg_adm1_historical.R
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+++ b/data-raw/chirps_monthly_afg_adm1_historical.R
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+#' `chirps_monthly_afg_adm1_historical.csv`
+#' `Date:` 2024-04-22
+#' This script generates the chirps_monthly_afg_adm1_historical.csv
+#' 
+#' **Description:**
+#' Using GEE temporally aggregate CHIRPS daily data to monthly.
+#' We then run zonal means  for each yr_mo combination at the admin-1 level 
+#' For all of Afghanistan.
+#' 
+#' **Tip:* 
+#' Script can be run in the background (on Mac with terminal call)
+#' `caffeinate -i -s Rscript data-raw/chirps_monthly_afg_adm1.R`
+
+library(rgee)
+library(tidyrgee)
+library(rhdx)
+library(tidyverse)
+ee_Initialize()
+
+overwrite_csv <-  c(T,F)[2]
+
+chirps_daily_url <- "UCSB-CHG/CHIRPS/DAILY"
+chirps_ic <- ee$ImageCollection(chirps_daily_url)
+
+adm1_fc <- ee$FeatureCollection("FAO/GAUL_SIMPLIFIED_500m/2015/level1")
+
+# filter adm1 to get only those in Afghanistan
+adm1_afghanistan <- adm1_fc$filter(ee$Filter$eq("ADM0_NAME", "Afghanistan"))
+
+# Add to map to quickly check - only for interactive viewing (not backaground job)
+# Map$addLayer(adm1_afghanistan, list(color = "red"), "Afghanistan")
+
+# convert to tidyee IC for easy temporal aggregation
+chirps_tic <-  as_tidyee(chirps_ic)
+
+chirps_monthly_tic <- chirps_tic |> 
+  group_by(year, month) |> 
+  summarise(
+    stat= "sum"
+  )
+
+
+# zonal means for each year-month at admin 1
+df_chirps_monthly <- ee_extract_tidy(
+  x=chirps_monthly_tic,
+  y= adm1_afghanistan, 
+  scale = 5566, 
+  stat = "mean",
+  via = "drive"
+)
+
+# write as csv
+df_csv_outpath <-file.path(
+  Sys.getenv("AA_DATA_DIR_NEW"),
+  "public",
+  "processed",
+  "afg",
+  "chirps_monthly_afg_adm1_historical.csv"
+  ) 
+
+if(overwrite_csv){
+  write_csv(x = df_chirps_monthly,
+            file = df_csv_outpath)
+}
+