  Collecting time series data spatially distributed in many locations is often
important for analyzing climate change and its impacts on ecosystems. However,
comprehensive spatial data collection is not always feasible, requiring us to
predict climate variables at some locations. This study focuses on a prediction
of climatic elements, specifically near-surface temperature and pressure, at a
target location apart from a data observation point. Our approach uses two
prediction methods: reservoir computing (RC), known as a machine learning
framework with low computational requirements, and vector autoregression models
(VAR), recognized as a statistical method for analyzing time series data. Our
results show that the accuracy of the predictions degrades with the distance
between the observation and target locations. We quantitatively estimate the
distance in which effective predictions are possible. We also find that in the
context of climate data, a geographical distance is associated with data
correlation, and a strong data correlation significantly improves the
prediction accuracy with RC. In particular, RC outperforms VAR in predicting
highly correlated data within the predictive range. These findings suggest that
machine learning-based methods can be used more effectively to predict climatic
elements in remote locations by assessing the distance to them from the data
observation point in advance. Our study on low-cost and accurate prediction of
climate variables has significant value for climate change strategies.
