WorldClim_Climate data:: Automatic Downloading, Processing, and Visualizing Climate Data in R

📜 Description: In this video, I demonstrate how to download, process, and visualize high-resolution climate data (temperature and precipitation) from WorldClim Version 2.1 using R. This workflow covers both historical climate data (1970–2000) and future climate projections (2021–2060) under CMIP6 scenarios (SSP245 and SSP585). The case study focuses on the Missouri state (USA), but you can easily adapt this workflow to any region of the world. 🧠 What You Will Learn: ✅ How to download WorldClim V2.1 data for multiple variables using automated R scripts ✅ How to extract regional climate data using shapefiles and terra, sf, and data.table ✅ How to process and organize climate rasters (Tmin, Tmax, Precipitation) into tidy data frames ✅ How to analyze monthly and scenario-based climate variations (e.g., August–November) ✅ How to visualize spatiotemporal changes using advanced ggplot2 mapping and facet grids ✅ How to compare historical and future climate distributions using boxplots and densigrams ✅ How to analyze correlations between precipitation and temperature (Tmin, Tmax) ✅ How to combine multiple plots (maps + statistical plots) using cowplot and patchwork 📁 Data Source: https://www.worldclim.org/data/index.... 🧠 Rscript: https://github.com/alihassan720/Geo_S... #rstudio #rstats #gfas #copernicus #wildfire #wildfires #wildfirenews #wildfireimpact #wildfiremanagement #remotesensing #airquality #airqualityindex #airqualitymonitor #gis #consultation #datascience #climatechange #climatedata #geospatialtechnology #geospatial #rprogrammingforbeginners #california #californiarealestate #ucdavis #washu #ucsb #usa