Abstract Bioclimatic variables, widely used in ecological and biogeographical studies, are typically derived from 30‐year averages of monthly data (e.g. 1971–2000). Unfortunately, the use of these long‐term averaged variables often creates a temporal mismatch with the observational data collected, which potentially undermines how environmental conditions at a specific time could influence ecological processes. For example, as global temperatures and precipitation patterns shift, relying on these averages can lead to inaccurate assessments of the environmental conditions, thus hindering the recognition of phenological events or species responses to recent climate change. While high‐resolution (e.g. months, weeks, days) time‐series data are available, a dedicated and efficient tool for generating variables over custom time intervals from them has been lacking. The fastbioclim R package addresses this gap by generating bioclimatic and derived environmental summary variables from supplied raster data for user‐defined timeframes. This efficient design accelerates large‐scale raster processing on standard personal computers. Key features allow users to select a subset of variables, define time periods beyond standard quarters and summarise data from finer time units such as weeks or days. Furthermore, fastbioclim extends the standard set of 19 variables by incorporating variables based on moisture and solar radiation (bio20–35) and by summarising other variables like wind speed, evapotranspiration or any other time‐series data available, using the same standardised methodology. The package's capabilities are demonstrated with two contrasting examples: the creation of a long‐term averaged, extended set of 35 bioclimatic variables for Mexico (2011–2020) and the generation of a time series of derived summary variables from cloud cover data for Madagascar. By streamlining the creation of temporally matched environmental variables, fastbioclim enhances the quality of environmental data for more temporally accurate ecological and biogeographical studies. This is particularly valuable for developing ecological models that incorporate the temporal dimension and for enhancing the understanding of how environmental conditions drive specific biological processes, including, but not limited to, the assessment of species' vulnerability to recent climate shifts.
Pinilla‐Buitrago et al. (Wed,) studied this question.