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A three-step climate classification was applied to a spatial domain coveringthe Himalayan arc and adjacent plains regions using input data from fourglobal meteorological reanalyses. Input variables were selected based on anunderstanding of the climatic drivers of regional water resource variabilityand crop yields. Principal component analysis (PCA) of those variables and<i>k</i>-means clustering on the PCA outputs revealed a reanalysis ensembleconsensus for eight macro-climate zones. Spatial statistics of inputvariables for each zone revealed consistent, distinct climatologies. Thisclimate classification approach has potential for enhancing assessmentof climatic influences on water resources and food security as well as forcharacterising the skill and bias of gridded data sets, both meteorologicalreanalyses and climate models, for reproducing subregional climatologies.Through their spatial descriptors (area, geographic centroid, elevation meanrange), climate classifications also provide metrics, beyond simple changesin individual variables, with which to assess the magnitude of projectedclimate change. Such sophisticated metrics are of particular interest forregions, including mountainous areas, where natural and anthropogenicsystems are expected to be sensitive to incremental climate shifts

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources