I know that resampling from coarse resolution to fine resolution is bad. Effectively you are making up data. However, if the study area is small are there any other options? Worldclim comes at around 900m resolution at it's finest. But running an SDM (species distribution model) on a small study area is relatively pointless as the the 900 x 900 cells are too big. The easiest option is to resample the worldclim data to 30m. Worldclim data is interpolated from weather stations. Would this count as a further interpolation or would it create nonsense outputs?
Generally, resampling this kind of data using interpolation will probably lead to poor results, especially if the area concerned is mountainous (as whuber pointed out, microscale climate data is spatially highly variable and interpolates poorly). Increasing the resolution thirty times is however rather drastic and I'd think twice about the very relevance of such data. If you consider doing so, I'd search this answer for some methods.
However, a better way is to use cokriging or other covariate interpolation methods together with other temperature predictors - the relief most likely being the primary factor here, but others such as land cover type and insolation could be useful too.