Spatial distribution and trends of different precipitation variability indices based on daily data in Northern Chile between 1966 and 2015

Northern Chile is one of the most arid regions in the world, as it includes the Atacama Desert. At high elevations, most precipitation is observed only during a short period of the year, from December until March. This renders water availability a major concern for policymakers. Accumulated rainfall varies considerably from one year to another, and for this reason, climate projections have a very low degree of confidence in this area. Consequently, in this region, it is more interesting to study the irregularity of precipitation itself than accumulated rainfall values, as they express in a clearer way the behaviour of precipitation. According to daily data from 161 meteorological stations, four irregularity indices of precipitation were calculated: concentration index, entropy, persistence index, and fractal dimension. These indices were measured according to observed values, and their spatial distribution was subsequently determined by interpolating following multivariate regression models that consider different geographical variables such as latitude, distance to the Amazon Basin, elevation, orientation, and curvature. The temporal trends of each index and for each meteorological station were also calculated, displaying different results depending on the latitude and elevation. These changes agree with the observed modifications on the inter‐tropical atmospheric circulation and with changes in the precipitation diurnal cycle. These results will help improve climate projections for this region, in the process facilitating the development of more accurate climate models and informing the formulation of water management policies. Water in Northern Chile, mainly characterized by the Atacama Desert, is a very scarce resource, and precipitations are the principal providing source of it. Under very challenging climate change scenarios, to study, not only the amount evolutions but also the irregularity of rainfall is of high interest, so models can be improved. Climate projections can be more confident, so policymakers can develop more appropriate politics and can take more accurate decisions to manage water in this area.