Peatlands are considered one of the biggest carbon reserves in the terrestrial ecosystem, comprising 30% of the present-day soil organic carbon pool (Yu, 2012). They are also a major source of methane (CH 4 ), a potent greenhouse gas (Abdalla et al., 2016). They are transitional zones between upland mineral soils and wetland ecosystems (Loisel et al., 2017). High latitude peatlands constitute unique habitats with many special characteristics such as shallow water table depth, organic soils, distinct vegetation cover dominated by bryophytes, spatial heterogeneity, anaerobic biogeochemistry and permafrost spread in cold regions making these systems an important component in the global carbon cycle (Loisel et al., 2017;Yu, 2012). Recent observations have shown that the vegetation structure, hydrology, and carbon balance are rapidly changing in many peatlands (Johansson et al., 2006;Pinceloup et al., 2020). These ongoing changes will disturb the prevailing land-atmosphere carbon balance and trigger some pertinent climate-relevant feedbacks (Belyea, 2013;Zhu & Zhuang, 2016). Studies have indicated that peatlands will continue to act as carbon sinks in the next decades under different warming scenarios, but there is a possibility that they become carbon neutral or even a minor carbon source by the end of this century (Chaudhary et al., 2017b(Chaudhary et al., , 2020Gallego-Sala et al., 2018;Qiu et al., 2020). To quantify and understand the overall effects of these rapid changes, various advanced peatland models have been employed, but often these models are forced with limited set of Representative Concentration Pathway (RCP) scenarios (Chaudhary et al., 2020;Müller & Joos, 2021;Qiu et al., 2020). It is a common trend to focus on two-three scenarios in modeling studies in order to obtain end-member estimates. All the climate scenarios are developed as a suite of complementary possibilities and the Intergovernmental Panel on Climate Change (IPCC) recommends that modeling studies should consider all the RCP scenarios for a complete understanding of system behavior in future conditions