Mapping Daily Air Temperature Over the Hawaiian Islands From 1990 to 2021 via an Optimized Piecewise Linear Regression Technique

Gridded air temperature data are required in various fields such as ecological modeling, weather forecasting, and surface energy balance assessment. In this work, a piecewise multiple linear regression model is used to produce high‐resolution (250 m) daily maximum (Tmax), minimum (Tmin), and mean (Tmean) near‐surface air temperature maps for the State of Hawaiʻi for a 32‐year period (1990–2021). Multiple meteorological and geographical variables such as the elevation, daily rainfall, coastal distance index, leaf area index, albedo, topographic position index, and wind speed are independently tested to determine the most well‐suited predictor variables for optimal model performance. During the mapping process, input data scarcity is addressed first by gap‐filling critical stations at high elevation using a predetermined linear relationship with other strongly‐correlated stations, and second, by supplementing the training dataset with station data from neighboring islands. Despite the numerous covariates physically linked to temperature, the most parsimonious model selection uses elevation as its sole predictor, and the inclusion of the additional variables results in increased cross‐validation errors. The mean absolute error of resultant estimated Tmax and Tmin maps over the Hawaiian Islands from 1990 to 2021 is 1.7°C and 1.3°C, respectively. Corresponding bias values are 0.01°C and −0.13°C, respectively for the same variables. Overall, the results show the proposed methodology can robustly generate daily air temperature maps from point‐scale measurements over complex topography. Plain Language Summary Uniformly gridded maps of daily air temperature are a valuable resource for scientific research, resource management, and environmental education and awareness. However, there are numerous challenges to producing these maps in the State of Hawai'i, including the steep changes in terrain, and the relatively limited availability of weather data measurements on which the maps are based. To account for these challenges, the daily air temperature maps are modeled based on other variables physically linked to temperature, such as elevation or rainfall, which are used as independent variables to predict the temperature values. After testing numerous combinations of variables, it was found that the simplest model using only elevation as the predictor is the most efficient combination with respect to balancing lower prediction errors with model complexity. Excluding o