Polylepis tarapacana is the highest-elevation tree species worldwide growing between 4000 and 5000 m a.s.l. along the South American Altiplano. P. tarapacana is adapted to live in harsh conditions and has been widely used for drought and precipitation tree-ring based reconstructions. Here, we present a 400-year tree-ring width (TRW) chronology located in southern Peru (17ºS; 69ºW) at the northernmost limit of P. tarapacana tree species distribution. The objectives of this study are to assess tree growth sensitivity of a northern P. tarapacana population to (1) precipitation, temperature and El Niño Southern Oscillation (ENSO) variability; (2) to compare its growth variability and ENSO sensitivity with southern P. tarapacana forests. Our results showed that this TRW record is highly sensitive to the prior summer season (Nov-Jan) precipitation (i.e. positive correlation) when the South American Summer Monsoon (SASM) reaches its maximum intensity in this region. We also found a positive relationship with current year temperature that suggests that radial growth may be enhanced by warm, less cloudy, conditions during the year of formation. A strong positive relationship was found between el Niño 3.4 and tree growth variability during the current growing season, but negative during the previous growth period. Growth variability in our northern study site was in agreement with other populations that represent almost the full range of P. tarapacana latitudinal distribution (~ 18ºS to 23ºS). Towards the south of the P. tarapacana TRW network there was a decrease in the strength of the agreement of growth variability with our site,with the exception of higher correlation with the two southeastern sites. Similarly, the TRW chronologies recorded higher sensitivity to ENSO influences in the north and southeastern locations, which are wetter, than the drier southwestern sites. These patterns hold for the entire period, as well as for periods of high and low ENSO activity. Overall, P. tarapacana tree growth at the north of its distribution is mostly influenced by prior year moisture availability and current year temperature that are linked to large-scale climate patterns such as the SASM and ENSO, respectively.