# Lightning-Ignited Wildfires beyond the Polar Circle
## 2 Materials and Methods
### Environmental Data  
We analyzed fire occurrence rate versus the following variables: lightning strikes' occurrence, air temperature, drought index SPEI, precipitation, soil moisture, and terrestrial water content. The World Wide Lightning Location Network (WWLLN) provided the lightning strikes occurrence data for the entire Arctic for 2010-2020 [10; 11]. We assume that the detection efficiency of the WWLLN lightning is uniform throughout the Arctic. We generate the lightning strikes index (LSI), which is a normalized unitless metric that varies between 0.0 (no lightning) and 1.0 (maximal number of strikes for the entire period [5].  
Monthly air temperature, precipitation, and soil moisture content (0-7 cm depth) were obtained at a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) from the ERA5-Land dataset [42]. In the statistical analysis, we used temperature, precipitation, and moisture data obtained within the location of each given hotspot. The drought index SPEI (the Standardized Precipitation Evapotranspiration Index) monthly values were obtained from the Global Drought Monitor ([[https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)]([https://spei.csic.es/map/maps.html](https://spei.csic.es/map/maps.html)) (accessed 27 March 2023)). The SPEI is a measure of air moisture content, and it is defined as the difference between total precipitation and potential evapotranspiration [43]. The terrestrial water content (or EWTA, Equivalent of Water Thickness Anomalies; hereafter TW) is the product of gravimetric measurements (in cm\({}^{-1}\)) by the GRACE/GRACE-FO satellites with on-ground resolution 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\)[44], which is characteristic of the moisture regime and represents total terrestrial water storage. Monthly EWTA data were obtained from the GFZ database ([[https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3](https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3)]([https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3](https://podaac-opendap.jpl.nasa.gov/opendap/hyrax/allData/tellus/L3)) accessed on 15 October 2022). Temporal dynamics of air temperature, precipitation, soil moisture, terrestrial water content, and SPEI drought index in Arctic sectors are presented in Figure S1 (from Supplementary Material) for the analyzed period 2001-2022.  
To estimate vegetation recovery after fire, we analyzed the temporal dynamics of the gross primary productivity (GPP) for the 325,352 distinct fire locations within the Arctic Circle. GPP values were obtained from the Terra/MODIS product MOD17A3 HGF at temporal and spatial resolutions of 8 days and 500 m\({}^{2}\), respectively ([[https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)]([https://search.earthdata.nasa.gov](https://search.earthdata.nasa.gov)) (accessed on 27 March 2023)) [45].  
To ensure statistically significant results for the calculations of trends, the minimal time interval used was equal to five years since the year of the fire. Consequently, trends data covered the period from 2001 until 2018. Trends were calculated based on the Theil-Sen non-parametric estimator [46; 47]. This estimator is more accurate than linear regression [48]. We obtained the Theil-Sen estimator in Python programming language from the library pymannkendall 1.4.2 ([[https://pypi.org/project/pymannkendall](https://pypi.org/project/pymannkendall)]([https://pypi.org/project/pymannkendall](https://pypi.org/project/pymannkendall)) (accessed on 5 February 2023))). We applied the estimator in ESRI ArcGIS software to analyze the spatial distribution of GPP for 2001-2022.