DOI: 10.5061/dryad.cfxpnvxkp
title: Global 0.25° daily observed and counterfactual fire weather index (1979–2024)

description: This is the associated dataset and code for the paper "Increasing
 Synchronicity of Global Extreme Fire Weather." To identify
 synchronous fire weather, we calculate daily Fire Weather Index (FWI) at
 0.25° resolution for the period 1979–2024 using fifth-generation European
 Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) data,
 based on daily maximum air temperature, daily minimum relative humidity,
 daily mean wind speed, and daily total precipitation. We also apply an
 overwintering procedure in our calculations that accounts for
 inter-seasonal drought in cold climates. To assess the contribution of
 anthropogenic climate change, we apply the same methodology to derive
 counterfactual FWI that removes the first-order influence of modeled
 climate change in the variables used to calculate FWI. The counterfactual
 is constructed by subtracting the low-pass filtered signal of monthly
 changes in temperature, humidity, wind speed, and precipitation relative
 to a quasi-preindustrial climate (1850–1900) as simulated by the
 multi-model mean of 20 models participating in the Coupled Model
 Intercomparison Project Phase 6 (CMIP6) from the observational record.
description: # Global 0.25° daily observed and counterfactual fire weather index
 (1979–2024) Dataset DOI:
 [10.5061/dryad.cfxpnvxkp](10.5061/dryad.cfxpnvxkp) ## Description of the
 data and file structure We calculate daily Fire Weather Index (FWI) at
 0.25° resolution for the period 1979–2024 using ERA5 reanalysis data,
 based on daily maximum temperature, minimum relative humidity, mean wind
 speed, and total precipitation. An overwintering procedure is applied to
 account for inter-seasonal drought in cold climates. To derive
 counterfactual FWI, we apply the same methodology to input fields adjusted
 to remove the first-order influence of anthropogenic climate change. This
 adjustment is made by subtracting the low-pass filtered monthly climate
 change signal (relative to 1850–1900) from ERA5, using the multi-model
 mean of 20 CMIP6 models: ACCESS-CM2, AWI-CM-1-1-MR, CanESM5-CanOE,
 CMCC-ESM2, CNRM-CM6-1-HR, CNRM-CM6-1, CNRM-ESM2-1, EC-Earth3-CC,
 EC-Earth3-Veg-LR, FIO-ESM-2-0, GFDL-ESM4, HadGEM3-CG31-LL, INM-CM4-8,
 INM-CM5-0, IPSL-CM6A-LR, MIROC6, MIROC-ES2L, MPI-ESM1-2-LR, MRI-ESM2-0,
 UKESM1-0-LL. ### Files and variables **File**: fwi_era5_YYYY.nc *
 **Description**: Daily observed FWI for the year YYYY **File**:
 fwi_era5_counter_YYYY.nc * **Description**: Daily counterfactual FWI for
 the year YYYY **File**: data_preprocess_observed.ipynb *
 **Description**: Python script for preprocessing observed FWI data
 **File**: data_preprocess_counterfactual.ipynb * **Description**: Python
 script for preprocessing counterfactual FWI data **File**: figX.ipynb *
 **Description**: Python script for analysis and plotting of Figure X
 **File**: SFW_plot.R * **Description**: R script for plotting ##
 Code/software ### 1. Panoply Panoply is a cross-platform application
 developed by NASA for viewing NetCDF, HDF, and GRIB datasets. It can be
 used to quickly visualize the daily observed and counterfactual FWI .nc
 files. Download:
 [https://www.giss.nasa.gov/tools/panoply/](https://www.giss.nasa.gov/tools/panoply/) No coding required—suitable for users interested in basic plotting, slicing, and exporting maps or time series. ### 2. Python Used for preprocessing, analysis, and visualization of the Fire Weather Index datasets. Key packages: * xarray, numpy, pandas: data handling and transformation * matplotlib, seaborn: plotting * rioxarray, cartopy (optional): spatial visualization - scipy: statistical operations ### 3. R Used for advanced plotting and pattern analysis. Key packages: ggplot2, raster, tidyverse ### 4. Workflow Summary Explore .nc files directly using Panoply Preprocess and analyze using Python notebooks Generate plots and figures using Jupyter and R scripts ## Access information Data was derived from the following sources: * [https://doi.org/10.24381/cds.4991cf48](https://doi.org/10.24381/cds.4991cf48) * [https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)