This study investigates the impact of climate-specific finance on extreme temperature patterns. It uses annual average maximum temperature as the dependent variable, with adaptation finance, CO2 emissions, and inflation as explanatory variables. The analysis focuses on India, Bangladesh, and Nepal over the period 2011 to 2020 and applied a panel Autoregressive Distributed Lag model to capture both short- and long-run relationships. In the long run, the results reveal that adaptation finance has a negative association with annual maximum temperature, whereas CO2 emissions and inflation show positive associations. This negative relationship reflects the role of adaptation finance in the development of infrastructure, the transfer of technology, and the enhancement of institutional capacity to counter rising temperatures. However, their influence is weak. In contrast, the positive association of CO2 emissions with temperature suggests that rising emissions remain a key driver of climate stress in the region. Similarly, the positive relationship between inflation and temperature shows that high inflation levels reduce the capacity to allocate resources for climate adaptation and lead to greater exposure to climate risks. Furthermore, the error correction term (− 1.18), which is significant at the 10% level, indicates a relatively fast and stable return to long-run equilibrium. Hence, the study suggests that policymakers of the respective countries must prioritize emission reduction, macroeconomic stability, and sustained adaptation finance to address temperature-related climate risks.