Simulating precipitation in the Northeast United States using a climate‐informed K‐nearest neighbour algorithm

Abstract

Decadal prediction using climate models faces long‐standing challenges. While global climate models may reproduce long‐term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate‐informed K‐nearest neighbour (K‐NN)‐based stochastic modelling approach to capture the long‐term trend and variability while replicating intra‐annual statistics. The climate‐informed K‐NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near‐term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate‐informed K‐NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K‐NN weather generator and ISIMIP‐2b GFDL general circulation model bias‐corrected output in a cross‐validation mode. Results indicate that the climate‐informed K‐NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within‐year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra‐annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near‐term streamflow, which in turn can be used for short‐term water resources planning and management.