Assessing the performance of satellite derived and reanalyses data in capturing seasonal changes in extreme precipitation scaling rates over the Indian subcontinent

Intensity and frequency of short duration precipitation extremes are expected to increase under a warming climate at approximate to 7%K 1, following the Clausius-Claypeyron scaling relationship. Recent studies have analyzed global and regional scaling rates for precipitation extremes against temperature over an annual period, however assessment of seasonal variations of precipitation-temperature scaling, is largely unexplored, especially over the Indian subcontinent. Satellite derived and reanalysis based precipitation data sets can function as a suitable alternative to rain-gauge based data sets over data-sparse regions. In the present study, the performance of three high resolution data sets -GPM-IMERG satellite derived, ERA5 and IMDAA reanalysis precipitation -in deter-mining the seasonal variations in precipitation-temperature scaling rates are investigated. When compared with the IMD data, IMERG and IMDAA capture the spatial variations and magnitude of scaling rates of daily pre-cipitation extremes much better than ERA5. Two calibrated datasets -AIMERG and AERA5 are also analyzed and compared to IMD scaling rates. Following this, we further probe into the performance of the IMERG, ERA5 and IMDAA data sets over the entire Indian subcontinent, by including the results of scaling rates over oceans. The scaling rates of all three are found to be comparable in terms of magnitude and spatial distribution when analyzed over the entire Indian subcontinent. Significant seasonal variations in scaling rates are found over the Indian subcontinent, with the highest scaling rates in the post-monsoon and pre-monsoon seasons, and weaker rates in the monsoon season. Land-ocean contrast in scaling rates is evident across all seasons, but weak ocean scaling rates are found in the monsoon season for all three data sets.

Automated summary

According to the study, the intensity and frequency of short duration precipitation extremes are expected to increase at a rate of approximately 7% per K under a warming climate. Previous research has analyzed the relationship between precipitation extremes and temperature on an annual basis, but the assessment of seasonal variations in precipitation-temperature scaling, especially over the Indian subcontinent, remains largely unexplored. This study investigates the performance of three high-resolution datasets in determining the seasonal variations in precipitation-temperature scaling rates.