Assessing the performance of climate reanalysis datasets in capturing hot and cold extremes and their trends in India.

A significant portion of the Earth's surface lacks long-term in-situ measurement of essential meteorological variables. Climate reanalysis serves as a valuable alternative to historical observations by providing homogenous and complete records of several atmospheric variables, especially in data-sparse regions.  Reanalysis is produced by assimilating sparse observational data from a variety of sources into numerical weather prediction models that solve the dynamics of land, ocean, and atmospheric processes for analyzed periods. Recent generation reanalysis is now available at finer spatial and temporal resolutions, making them lucrative for hydrological and climatological studies. However, reanalysis has inherent biases that necessitate their evaluation before such application. While the assessment of reanalysis datasets is common in representing mean climatology on a daily, monthly, or seasonal scale, their ability to capture the spatial pattern of extreme temperature events and their trends remains controversial.

By comparing seven such reanalysis datasets over India (ERA5-Land, ERA5, MERRA2, CFSR, JRA55, IMDAA, and EARS) it is found that the newest generation reanalysis having a higher resolution, better captures the magnitude, frequency, and duration of hot and cold extremes. The reanalysis datasets are compared with a gauge-based gridded temperature dataset from the India Meteorological Department (IMD) to assess their suitability in representing extreme temperature events and their trends over India. For evaluation, several extreme temperature indices are calculated based on the recommendation of ETCCDI, covering the frequency, intensity, and duration of hot and cold extreme temperature events. It is also found that in response to global warming, extreme hot events are rising, and extreme cold events are decreasing in India which is also captured by most of the reanalysis. However, the reanalysis estimated trend areas and magnitudes are not similar when compared to trends with a regional station-based gridded dataset. Thus, care should be taken in selecting datasets for such applications and interpreting their trends.