ABSTRACT The Coupled Model Inter‐comparison Project phase 6 (CMIP6) provides a suite of general circulation models (GCMs) and Socioeconomic Shared Pathways (SSPs) primarily for continental‐scale climate assessments. However, adapting these models for sub‐national assessments, particularly in countries with varied geography like Ecuador, and for complex variables such as precipitation, introduces challenges, including uncertainties in selecting appropriate GCMs and SSPs. To address these issues, we adopt a biogeographical approach that integrates regional climatic variations. Our analysis explores 26 GCMs, four SSP scenarios and four 20‐year time frames from WorldClim to evaluate discrepancies between the GCM precipitation projections, historical data and national climate projections across five Ecuadorian bioregions. This approach enabled us to sort the GCMs by annual precipitation medians, classify their monthly precipitation using Dynamic Time Warping (DTW) clustering, and develop ensembles highlighting both the largest and average precipitation anomalies within and beyond the bioregions. Among the 26 models examined, 16 projected an increase in annual precipitation in Ecuador, especially during the wet seasons, with the BCC‐CSM2‐MR model showing peak values, notably in the Choco region and eastern Amazon basin. Conversely, 10 models, with CMCC‐ESM2 showing the largest decreases, projected reduced precipitation across almost all Ecuadorian territories, except the Choco region. The largest reductions were in the Amazon basin, raising concerns about reduced precipitation. Discrepancies, primarily in the Andes and Galapagos bioregions, reveal the challenges posed by their complex topography and insular environments. While the GCMs captured spatial patterns of ENSO, our research was constrained to 20‐year averages, making direct comparison with historical records infeasible, highlighting the need for further research with shorter time frames and finer spatial resolutions. The variability in precipitation was linked to geographical factors, GCM configurations and unexpected SSP outcomes. Therefore, selecting GCMs and climatic indices tailored to specific bioregions is recommended for effective climate change impact assessments.
