The study analysed the changes in the rainfall, extreme indices and their future projections over Rajasthan state based on observed gridded datasets (1976-2005) and simulated climate models. The climate projections from two global circulation models (HadCM3 and GFCM21) are used in statistical downscaling tool LARS-WG5 (Long Ashton Research Station-Weather Generator) to generate future precipitation. Further, the changes in precipitation pattern are investigated for the baseline period and the future periods based on seven extreme precipitation indices. Three future periods are used for the analysis i.e., early century period 2011-2040 (2025s), a mid-century period of 2041-2070 (2055s) and a late-century period of 2071-2100 (2085s). The study area is classified in three regions based on elevation range i.e., region 1 (<250m), region 2 (251-350m) and region 3 (350-1700m). Based on results, it is observed that there is a possible decrease in monsoon precipitation at many grid points for all the three future periods. The maximum decrease in rainfall (-142mm) is observed in Banswara for the period 2041-2070, while the maximum increase (37mm) is found in Alwar along with Churu 1 and Ganganagar during the period 2071-2100. Consecutive dry days (CDD) is predicted to increase in the west and south-west direction, while it shows decrease values in eastern and central part of the study area with the maximum value in Ajmer district. The pattern in PRCPTOT revealed maximum negative change (- 90mm) in southern parts, and maximum positive change in the northern regions (62.2mm) in Churu 1. Further, R20 and RX5day are projected to decrease in all three regions in future with several magnitudes. For RX1day, a maximum positive change is observed in eastern parts (Jhalawar, Sawai Madhopur) and negative changes in the southern part of the study area. In case of R95p index, both positive and negative changes are observed. Similarly, the SDII indicates a positive change in 2011-2040 and negative changes for the remaining two future periods. Finally, SDII shows maximum positive changes in the south and southeastern regions (Jhalawar, Chittaurgarh) and positive changes in various parts with spatial and temporal changes. The results will help water resources planner to understand the change pattern in various precipitation indices in water scarce state of India.
